[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"similar-kmario23--deep-learning-drizzle":3,"tool-kmario23--deep-learning-drizzle":61},[4,18,26,36,44,53],{"id":5,"name":6,"github_repo":7,"description_zh":8,"stars":9,"difficulty_score":10,"last_commit_at":11,"category_tags":12,"status":17},4358,"openclaw","openclaw\u002Fopenclaw","OpenClaw 是一款专为个人打造的本地化 AI 助手，旨在让你在自己的设备上拥有完全可控的智能伙伴。它打破了传统 AI 助手局限于特定网页或应用的束缚，能够直接接入你日常使用的各类通讯渠道，包括微信、WhatsApp、Telegram、Discord、iMessage 等数十种平台。无论你在哪个聊天软件中发送消息，OpenClaw 都能即时响应，甚至支持在 macOS、iOS 和 Android 设备上进行语音交互，并提供实时的画布渲染功能供你操控。\n\n这款工具主要解决了用户对数据隐私、响应速度以及“始终在线”体验的需求。通过将 AI 部署在本地，用户无需依赖云端服务即可享受快速、私密的智能辅助，真正实现了“你的数据，你做主”。其独特的技术亮点在于强大的网关架构，将控制平面与核心助手分离，确保跨平台通信的流畅性与扩展性。\n\nOpenClaw 非常适合希望构建个性化工作流的技术爱好者、开发者，以及注重隐私保护且不愿被单一生态绑定的普通用户。只要具备基础的终端操作能力（支持 macOS、Linux 及 Windows WSL2），即可通过简单的命令行引导完成部署。如果你渴望拥有一个懂你",349277,3,"2026-04-06T06:32:30",[13,14,15,16],"Agent","开发框架","图像","数据工具","ready",{"id":19,"name":20,"github_repo":21,"description_zh":22,"stars":23,"difficulty_score":10,"last_commit_at":24,"category_tags":25,"status":17},3808,"stable-diffusion-webui","AUTOMATIC1111\u002Fstable-diffusion-webui","stable-diffusion-webui 是一个基于 Gradio 构建的网页版操作界面，旨在让用户能够轻松地在本地运行和使用强大的 Stable Diffusion 图像生成模型。它解决了原始模型依赖命令行、操作门槛高且功能分散的痛点，将复杂的 AI 绘图流程整合进一个直观易用的图形化平台。\n\n无论是希望快速上手的普通创作者、需要精细控制画面细节的设计师，还是想要深入探索模型潜力的开发者与研究人员，都能从中获益。其核心亮点在于极高的功能丰富度：不仅支持文生图、图生图、局部重绘（Inpainting）和外绘（Outpainting）等基础模式，还独创了注意力机制调整、提示词矩阵、负向提示词以及“高清修复”等高级功能。此外，它内置了 GFPGAN 和 CodeFormer 等人脸修复工具，支持多种神经网络放大算法，并允许用户通过插件系统无限扩展能力。即使是显存有限的设备，stable-diffusion-webui 也提供了相应的优化选项，让高质量的 AI 艺术创作变得触手可及。",162132,"2026-04-05T11:01:52",[14,15,13],{"id":27,"name":28,"github_repo":29,"description_zh":30,"stars":31,"difficulty_score":32,"last_commit_at":33,"category_tags":34,"status":17},1381,"everything-claude-code","affaan-m\u002Feverything-claude-code","everything-claude-code 是一套专为 AI 编程助手（如 Claude Code、Codex、Cursor 等）打造的高性能优化系统。它不仅仅是一组配置文件，而是一个经过长期实战打磨的完整框架，旨在解决 AI 代理在实际开发中面临的效率低下、记忆丢失、安全隐患及缺乏持续学习能力等核心痛点。\n\n通过引入技能模块化、直觉增强、记忆持久化机制以及内置的安全扫描功能，everything-claude-code 能显著提升 AI 在复杂任务中的表现，帮助开发者构建更稳定、更智能的生产级 AI 代理。其独特的“研究优先”开发理念和针对 Token 消耗的优化策略，使得模型响应更快、成本更低，同时有效防御潜在的攻击向量。\n\n这套工具特别适合软件开发者、AI 研究人员以及希望深度定制 AI 工作流的技术团队使用。无论您是在构建大型代码库，还是需要 AI 协助进行安全审计与自动化测试，everything-claude-code 都能提供强大的底层支持。作为一个曾荣获 Anthropic 黑客大奖的开源项目，它融合了多语言支持与丰富的实战钩子（hooks），让 AI 真正成长为懂上",144730,2,"2026-04-07T23:26:32",[14,13,35],"语言模型",{"id":37,"name":38,"github_repo":39,"description_zh":40,"stars":41,"difficulty_score":32,"last_commit_at":42,"category_tags":43,"status":17},2271,"ComfyUI","Comfy-Org\u002FComfyUI","ComfyUI 是一款功能强大且高度模块化的视觉 AI 引擎，专为设计和执行复杂的 Stable Diffusion 图像生成流程而打造。它摒弃了传统的代码编写模式，采用直观的节点式流程图界面，让用户通过连接不同的功能模块即可构建个性化的生成管线。\n\n这一设计巧妙解决了高级 AI 绘图工作流配置复杂、灵活性不足的痛点。用户无需具备编程背景，也能自由组合模型、调整参数并实时预览效果，轻松实现从基础文生图到多步骤高清修复等各类复杂任务。ComfyUI 拥有极佳的兼容性，不仅支持 Windows、macOS 和 Linux 全平台，还广泛适配 NVIDIA、AMD、Intel 及苹果 Silicon 等多种硬件架构，并率先支持 SDXL、Flux、SD3 等前沿模型。\n\n无论是希望深入探索算法潜力的研究人员和开发者，还是追求极致创作自由度的设计师与资深 AI 绘画爱好者，ComfyUI 都能提供强大的支持。其独特的模块化架构允许社区不断扩展新功能，使其成为当前最灵活、生态最丰富的开源扩散模型工具之一，帮助用户将创意高效转化为现实。",107888,"2026-04-06T11:32:50",[14,15,13],{"id":45,"name":46,"github_repo":47,"description_zh":48,"stars":49,"difficulty_score":32,"last_commit_at":50,"category_tags":51,"status":17},4721,"markitdown","microsoft\u002Fmarkitdown","MarkItDown 是一款由微软 AutoGen 团队打造的轻量级 Python 工具，专为将各类文件高效转换为 Markdown 格式而设计。它支持 PDF、Word、Excel、PPT、图片（含 OCR）、音频（含语音转录）、HTML 乃至 YouTube 链接等多种格式的解析，能够精准提取文档中的标题、列表、表格和链接等关键结构信息。\n\n在人工智能应用日益普及的今天，大语言模型（LLM）虽擅长处理文本，却难以直接读取复杂的二进制办公文档。MarkItDown 恰好解决了这一痛点，它将非结构化或半结构化的文件转化为模型“原生理解”且 Token 效率极高的 Markdown 格式，成为连接本地文件与 AI 分析 pipeline 的理想桥梁。此外，它还提供了 MCP（模型上下文协议）服务器，可无缝集成到 Claude Desktop 等 LLM 应用中。\n\n这款工具特别适合开发者、数据科学家及 AI 研究人员使用，尤其是那些需要构建文档检索增强生成（RAG）系统、进行批量文本分析或希望让 AI 助手直接“阅读”本地文件的用户。虽然生成的内容也具备一定可读性，但其核心优势在于为机器",93400,"2026-04-06T19:52:38",[52,14],"插件",{"id":54,"name":55,"github_repo":56,"description_zh":57,"stars":58,"difficulty_score":10,"last_commit_at":59,"category_tags":60,"status":17},4487,"LLMs-from-scratch","rasbt\u002FLLMs-from-scratch","LLMs-from-scratch 是一个基于 PyTorch 的开源教育项目，旨在引导用户从零开始一步步构建一个类似 ChatGPT 的大型语言模型（LLM）。它不仅是同名技术著作的官方代码库，更提供了一套完整的实践方案，涵盖模型开发、预训练及微调的全过程。\n\n该项目主要解决了大模型领域“黑盒化”的学习痛点。许多开发者虽能调用现成模型，却难以深入理解其内部架构与训练机制。通过亲手编写每一行核心代码，用户能够透彻掌握 Transformer 架构、注意力机制等关键原理，从而真正理解大模型是如何“思考”的。此外，项目还包含了加载大型预训练权重进行微调的代码，帮助用户将理论知识延伸至实际应用。\n\nLLMs-from-scratch 特别适合希望深入底层原理的 AI 开发者、研究人员以及计算机专业的学生。对于不满足于仅使用 API，而是渴望探究模型构建细节的技术人员而言，这是极佳的学习资源。其独特的技术亮点在于“循序渐进”的教学设计：将复杂的系统工程拆解为清晰的步骤，配合详细的图表与示例，让构建一个虽小但功能完备的大模型变得触手可及。无论你是想夯实理论基础，还是为未来研发更大规模的模型做准备",90106,"2026-04-06T11:19:32",[35,15,13,14],{"id":62,"github_repo":63,"name":64,"description_en":65,"description_zh":66,"ai_summary_zh":66,"readme_en":67,"readme_zh":68,"quickstart_zh":69,"use_case_zh":70,"hero_image_url":71,"owner_login":72,"owner_name":73,"owner_avatar_url":74,"owner_bio":75,"owner_company":76,"owner_location":77,"owner_email":78,"owner_twitter":79,"owner_website":80,"owner_url":81,"languages":82,"stars":91,"forks":92,"last_commit_at":93,"license":78,"difficulty_score":94,"env_os":95,"env_gpu":95,"env_ram":95,"env_deps":96,"category_tags":99,"github_topics":101,"view_count":32,"oss_zip_url":78,"oss_zip_packed_at":78,"status":17,"created_at":122,"updated_at":123,"faqs":124,"releases":125},5313,"kmario23\u002Fdeep-learning-drizzle","deep-learning-drizzle","Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!","deep-learning-drizzle 是一个精心整理的深度学习与人工智能学习资源库，旨在帮助学习者系统掌握从基础理论到前沿应用的核心知识。它汇集了涵盖深度学习、强化学习、机器学习、计算机视觉、自然语言处理（NLP）、概率图模型及语音识别等多个领域的优质讲座与课程资料。\n\n面对 AI 领域知识更新快、学习路径分散的痛点，deep-learning-drizzle 通过结构化的目录将庞杂的内容分类呈现，让学习者能够循序渐进地建立直觉并深入理解技术原理。正如 Geoffrey Hinton 教授所言，这里提供的丰富内容能帮助你“积累足够的知识以培养直觉，进而信任直觉去探索”。\n\n该资源库特别适合希望系统提升理论水平的开发者、科研人员以及高校学生使用。无论是想要夯实机器学习基础的初学者，还是寻求在特定领域（如现代计算机视觉或自动语音识别）深造的进阶用户，都能从中找到对应的学习指引。其独特亮点在于不仅关注热门的深度神经网络，还包含了优化方法、概率图模型等支撑 AI 发展的关键理论基础，为构建完整的知识体系提供了宝贵的一站式入口。","# :balloon: :tada: Deep Learning Drizzle :confetti_ball: :balloon:\n\n:books: [**\"Read enough so you start developing intuitions and then trust your intuitions and go for it!\"** ](https:\u002F\u002Fwww.deeplearning.ai\u002Fhodl-geoffrey-hinton\u002F) :books: ​\u003Cbr\u002F> Prof. Geoffrey Hinton, University of Toronto\n\n:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:\n\n### Contents\n:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:\n\n| | |\n| ------------------------------------------------------------ | ------------------------------------------------------------ |\n| **Deep Learning (Deep Neural Networks)** [:arrow_heading_down:](https:\u002F\u002Fgithub.com\u002Fkmario23\u002Fdeep-learning-drizzle#tada-deep-learning-deep-neural-networks-confetti_ball-balloon) | **Probabilistic Graphical Models** [:arrow_heading_down:](https:\u002F\u002Fgithub.com\u002Fkmario23\u002Fdeep-learning-drizzle#loudspeaker-probabilistic-graphical-models-sparkles) |\n| | |\n| **Machine Learning Fundamentals** [:arrow_heading_down:](https:\u002F\u002Fgithub.com\u002Fkmario23\u002Fdeep-learning-drizzle#cupid-machine-learning-fundamentals-cyclone-boom) | **Natural Language Processing** [:arrow_heading_down:](https:\u002F\u002Fgithub.com\u002Fkmario23\u002Fdeep-learning-drizzle#hibiscus-natural-language-processing-cherry_blossom-sparkling_heart) |\n| | |\n| **Optimization for Machine Learning** [:arrow_heading_down:](https:\u002F\u002Fgithub.com\u002Fkmario23\u002Fdeep-learning-drizzle#cupid-optimization-for-machine-learning-cyclone-boom) | **Automatic Speech Recognition** [:arrow_heading_down:](https:\u002F\u002Fgithub.com\u002Fkmario23\u002Fdeep-learning-drizzle#speaking_head-automatic-speech-recognition-speech_balloon-thought_balloon) |\n| | |\n| **General Machine Learning** [:arrow_heading_down:](https:\u002F\u002Fgithub.com\u002Fkmario23\u002Fdeep-learning-drizzle#cupid-general-machine-learning-cyclone-boom) | **Modern Computer Vision** [:arrow_heading_down:](https:\u002F\u002Fgithub.com\u002Fkmario23\u002Fdeep-learning-drizzle#fire-modern-computer-vision-camera_flash-movie_camera) |\n| | |\n| **Reinforcement Learning** [:arrow_heading_down:](https:\u002F\u002Fgithub.com\u002Fkmario23\u002Fdeep-learning-drizzle#balloon-reinforcement-learning-hotsprings-video_game) | **Boot Camps or Summer Schools** [:arrow_heading_down:](https:\u002F\u002Fgithub.com\u002Fkmario23\u002Fdeep-learning-drizzle#star2-boot-camps-or-summer-schools-maple_leaf) |\n| | |\n| **Bayesian Deep Learning** [:arrow_heading_down:](https:\u002F\u002Fgithub.com\u002Fkmario23\u002Fdeep-learning-drizzle#game_die-bayesian-deep-learning-spades-gem) | **Medical Imaging** [:arrow_heading_down:](https:\u002F\u002Fgithub.com\u002Fkmario23\u002Fdeep-learning-drizzle#movie_camera-medical-imaging-camera-video_camera) |\n| | |\n| **Graph Neural Networks** [:arrow_heading_down: ](https:\u002F\u002Fgithub.com\u002Fkmario23\u002Fdeep-learning-drizzle#tada-graph-neural-networks-geometric-dl-confetti_ball-balloon) | **Bird's-eye view of Artificial Intelligence** [:arrow_heading_down:](https:\u002F\u002Fgithub.com\u002Fkmario23\u002Fdeep-learning-drizzle#bird-birds-eye-view-of-agi-eagle) |\n| | |\n\n:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:\n## :tada: Deep Learning (Deep Neural Networks) :confetti_ball: :balloon: \n:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:\n\n| S.No | Course Name | University\u002FInstructor(s) | Course WebPage | Lecture Videos | Year |\n| ---- | ----------------------------------------------------- | ---------------------------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | --------------- |\n| 1. | **Neural Networks for Machine Learning** | Geoffrey Hinton, University of Toronto | [Lecture-Slides](http:\u002F\u002Fwww.cs.toronto.edu\u002F~hinton\u002Fcoursera_slides.html) \u003Cbr\u002F> [CSC321-tijmen](https:\u002F\u002Fwww.cs.toronto.edu\u002F~tijmen\u002Fcsc321\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLoRl3Ht4JOcdU872GhiYWf6jwrk_SNhz9) \u003Cbr\u002F> [UofT-mirror](https:\u002F\u002Fwww.cs.toronto.edu\u002F~hinton\u002Fcoursera_lectures.html) | 2012 \u003Cbr\u002F> 2014 |\n| 2. | **Neural Networks Demystified** | Stephen Welch, Welch Labs | [Suppl. Code](https:\u002F\u002Fgithub.com\u002Fstephencwelch\u002FNeural-Networks-Demystified) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLiaHhY2iBX9hdHaRr6b7XevZtgZRa1PoU) | 2014 |\n| 3. | **Deep Learning at Oxford** | Nando de Freitas, Oxford University | [Oxford-ML](http:\u002F\u002Fwww.cs.ox.ac.uk\u002Fteaching\u002Fcourses\u002F2014-2015\u002Fml\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLE6Wd9FR--EfW8dtjAuPoTuPcqmOV53Fu) | 2015 |\n| 4. | **Deep Learning for Perception** | Dhruv Batra, Virginia Tech | [ECE-6504](https:\u002F\u002Fcomputing.ece.vt.edu\u002F~f15ece6504\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL-fZD610i7yAsfH2eLBiRDa90kL2ML0f7) | 2015 |\n| 5. | **Deep Learning** | Ali Ghodsi, University of Waterloo | [STAT-946](https:\u002F\u002Fuwaterloo.ca\u002Fdata-analytics\u002Fdeep-learning) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLehuLRPyt1Hyi78UOkMPWCGRxGcA9NVOE) | F2015 |\n| 6. | **CS231n: CNNs for Visual Recognition** | Andrej Karpathy, Stanford University | [CS231n](http:\u002F\u002Fcs231n.stanford.edu\u002F2015\u002F) | `None` | 2015 |\n| 7. | **CS224d: Deep Learning for NLP** | Richard Socher, Stanford University | [CS224d](http:\u002F\u002Fcs224d.stanford.edu) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLmImxx8Char8dxWB9LRqdpCTmewaml96q) | 2015 |\n| 8. | **Bay Area Deep Learning** | Many legends, Stanford | `None` | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLrAXtmErZgOfMuxkACrYnD2fTgbzk2THW) | 2016 |\n| 9. | **CS231n: CNNs for Visual Recognition** | Andrej Karpathy, Stanford University | [CS231n](http:\u002F\u002Fcs231n.stanford.edu\u002F2016\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLkt2uSq6rBVctENoVBg1TpCC7OQi31AlC) \u003Cbr\u002F>[(Academic Torrent)](https:\u002F\u002Facademictorrents.com\u002Fdetails\u002F46c5af9e2075d9af06f280b55b65cf9b44eb9fe7) | 2016 |\n| 10. | **Neural Networks** | Hugo Larochelle, Université de Sherbrooke | [Neural-Networks](http:\u002F\u002Finfo.usherbrooke.ca\u002Fhlarochelle\u002Fneural_networks\u002Fcontent.html) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL6Xpj9I5qXYEcOhn7TqghAJ6NAPrNmUBH) \u003Cbr\u002F> [(Academic Torrent)](https:\u002F\u002Facademictorrents.com\u002Fdetails\u002Fe046bca3bc837053d1609ef33d623ee5c5af7300) | 2016 |\n| | | | | | |\n| 11. | **CS224d: Deep Learning for NLP** | Richard Socher, Stanford University | [CS224d](http:\u002F\u002Fcs224d.stanford.edu) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLlJy-eBtNFt4CSVWYqscHDdP58M3zFHIG) \u003Cbr\u002F>[(Academic Torrent)](https:\u002F\u002Facademictorrents.com\u002Fdetails\u002Fdd9b74b50a1292b4b154094b7338ec1d66e8894d) | 2016 |\n| 12. | **CS224n: NLP with Deep Learning** | Richard Socher, Stanford University | [CS224n](http:\u002F\u002Fweb.stanford.edu\u002Fclass\u002Fcs224n\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL3FW7Lu3i5Jsnh1rnUwq_TcylNr7EkRe6) | 2017 |\n| 13. | **CS231n: CNNs for Visual Recognition** | Justin Johnson, Stanford University | [CS231n](http:\u002F\u002Fcs231n.stanford.edu\u002F2017\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL3FW7Lu3i5JvHM8ljYj-zLfQRF3EO8sYv) \u003Cbr\u002F> [(Academic Torrent)](https:\u002F\u002Facademictorrents.com\u002Fdetails\u002Fed8a16ebb346e14119a03371665306609e485f13) | 2017 |\n| 14. | **Topics in Deep Learning** | Ruslan Salakhutdinov, CMU | [10707](https:\u002F\u002Fdeeplearning-cmu-10707.github.io\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLpIxOj-HnDsOSL__Buy7_UEVQkyfhHapa) | F2017 |\n| 15. | **Deep Learning Crash Course** | Leo Isikdogan, UT Austin | `None` | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLWKotBjTDoLj3rXBL-nEIPRN9V3a9Cx07) | 2017 |\n| 16. | **Deep Learning and its Applications** | François Pitié, Trinity College Dublin | [EE4C16](https:\u002F\u002Fgithub.com\u002Ffrcs\u002F4C16-2017) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLIo1iEzl5iB9NkulNR0X5vXN8AaEKglWT) | 2017 |\n| 17. | **Deep Learning** | Andrew Ng, Stanford University | [CS230](http:\u002F\u002Fcs230.stanford.edu\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLoROMvodv4rOABXSygHTsbvUz4G_YQhOb) | 2018 |\n| 18. | **UvA Deep Learning** | Efstratios Gavves, University of Amsterdam | [UvA-DLC](https:\u002F\u002Fuvadlc.github.io\u002F) | [Lecture-Videos](https:\u002F\u002Fuvadlc.github.io\u002Flectures-sep2018.html) | 2018 |\n| 19. | **Advanced Deep Learning and Reinforcement Learning** | Many legends, DeepMind | `None` | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLqYmG7hTraZDNJre23vqCGIVpfZ_K2RZs) | 2018 |\n| 20. | **Machine Learning** | Peter Bloem, Vrije Universiteit Amsterdam | [MLVU](https:\u002F\u002Fmlvu.github.io\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLCof9EqayQgsORO3pFzeYZFz6cszYO0VJ) | 2018 |\n| | | | | | |\n| 21. | **Deep Learning** | Francois Fleuret, EPFL | [EE-59](https:\u002F\u002Ffleuret.org\u002Fee559-2018\u002Fdlc) | [Video-Lectures](https:\u002F\u002Ffleuret.org\u002Fee559-2018\u002Fdlc\u002F#materials) | 2018 |\n| 22. | **Introduction to Deep Learning** | Alexander Amini, Harini Suresh and others, MIT | [6.S191](http:\u002F\u002Fintrotodeeplearning.com\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLtBw6njQRU-rwp5__7C0oIVt26ZgjG9NI) \u003Cbr\u002F> [2017-version](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLkkuNyzb8LmxFutYuPA7B4oiMn6cjD6Rs) | 2017- 2021 |\n| 23. | **Deep Learning for Self-Driving Cars** | Lex Fridman, MIT | [6.S094](https:\u002F\u002Fselfdrivingcars.mit.edu\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLrAXtmErZgOeiKm4sgNOknGvNjby9efdf) | 2017-2018 |\n| 24. | **Introduction to Deep Learning** | Bhiksha Raj and many others, CMU | [11-485\u002F785](http:\u002F\u002Fdeeplearning.cs.cmu.edu\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLp-0K3kfddPwJBJ4Q8We-0yNQEG0fZrSa) | S2018 |\n| 25. | **Introduction to Deep Learning** | Bhiksha Raj and many others, CMU | [11-485\u002F785](http:\u002F\u002Fdeeplearning.cs.cmu.edu\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLp-0K3kfddPyH44FP0dl0CbYprvTcfgOI) [Recitation-Inclusive](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLLR0_ZOlbfD6KDBq93G8-guHI-J1ICeFm) | F2018 |\n| 26. | **Deep Learning Specialization** | Andrew Ng, Stanford | [DL.AI](https:\u002F\u002Fwww.deeplearning.ai\u002Fdeep-learning-specialization\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCcIXc5mJsHVYTZR1maL5l9w\u002Fplaylists) | 2017-2018 |\n| 27. | **Deep Learning** | Ali Ghodsi, University of Waterloo | [STAT-946](https:\u002F\u002Fuwaterloo.ca\u002Fdata-analytics\u002Fteaching\u002Fdeep-learning-2017) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLehuLRPyt1HxTolYUWeyyIoxDabDmaOSB) | F2017 |\n| 28. | **Deep Learning** | Mitesh Khapra, IIT-Madras | [CS7015](https:\u002F\u002Fwww.cse.iitm.ac.in\u002F~miteshk\u002FCS7015.html) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLyqSpQzTE6M9gCgajvQbc68Hk_JKGBAYT) | 2018 |\n| 29. | **Deep Learning for AI** | UPC Barcelona | [DLAI-2017](https:\u002F\u002Ftelecombcn-dl.github.io\u002F2017-dlai\u002F) \u003Cbr\u002F> [DLAI-2018](https:\u002F\u002Ftelecombcn-dl.github.io\u002F2018-dlai\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL-5eMc3HQTBagIUjKefjcTbnXC0wXC_vd) | 2017-2018 |\n| 30. | **Deep Learning** | Alex Bronstein and Avi Mendelson, Technion | [CS236605](https:\u002F\u002Fvistalab-technion.github.io\u002Fcs236605\u002Finfo\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLM0a6Z788YAZuqg2Ip-_dPLzEd33lZvP2) | 2018 |\n| | | | | | |\n| 31. | **MIT Deep Learning** | Many Researchers, Lex Fridman, MIT | [6.S094, 6.S091, 6.S093](https:\u002F\u002Fdeeplearning.mit.edu\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLrAXtmErZgOeiKm4sgNOknGvNjby9efdf) | 2019 |\n| 32. | **Deep Learning Book** companion videos | Ian Goodfellow and others | [DL-book slides](https:\u002F\u002Fwww.deeplearningbook.org\u002Flecture_slides.html) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLsXu9MHQGs8df5A4PzQGw-kfviylC-R9b) | 2017 |\n| 33. | **Theories of Deep Learning** | Many Legends, Stanford | [Stats-385](https:\u002F\u002Fstats385.github.io\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLwUqqMt5en7fFLwSDa9V3JIkDam-WWgqy) \u003Cbr\u002F> (first 10 lectures) | F2017 |\n| 34. | **Neural Networks** | Grant Sanderson | `None` | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi) | 2017-2018 |\n| 35. | **CS230: Deep Learning** | Andrew Ng, Kian Katanforoosh, Stanford | [CS230](http:\u002F\u002Fcs230.stanford.edu\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLoROMvodv4rOABXSygHTsbvUz4G_YQhOb) | A2018 |\n| 36. | **Theory of Deep Learning** | Lots of Legends, Canary Islands | [DALI'18](http:\u002F\u002Fdalimeeting.org\u002Fdali2018\u002FworkshopTheoryDL.html) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLeCNfJWZKqxtWBnV8gefGqmmPgz9YF4LR) | 2018 |\n| 37. | **Introduction to Deep Learning** | Alex Smola, UC Berkeley | [Stat-157](http:\u002F\u002Fcourses.d2l.ai\u002Fberkeley-stat-157\u002Findex.html) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLZSO_6-bSqHQHBCoGaObUljoXAyyqhpFW) | S2019 |\n| 38. | **Deep Unsupervised Learning** | Pieter Abbeel, UC Berkeley | [CS294-158](https:\u002F\u002Fsites.google.com\u002Fview\u002Fberkeley-cs294-158-sp19\u002Fhome) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCf4SX8kAZM_oGcZjMREsU9w\u002Fvideos) | S2019 |\n| 39. | **Machine Learning** | Peter Bloem, Vrije Universiteit Amsterdam | [MLVU](https:\u002F\u002Fmlvu.github.io\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLCof9EqayQgupldnTvqNy_BThTcME5r93) | 2019 |\n| 40. | **Deep Learning on Computational Accelerators** | Alex Bronstein and Avi Mendelson, Technion | [CS236605](https:\u002F\u002Fvistalab-technion.github.io\u002Fcs236605\u002Flectures\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLM0a6Z788YAa_WCy_V-q9NrGm5qQegZR5) | S2019 |\n| | | | | | |\n| 41. | **Introduction to Deep Learning** | Bhiksha Raj and many others, CMU | [11-785](http:\u002F\u002Fwww.cs.cmu.edu\u002F~bhiksha\u002Fcourses\u002Fdeeplearning\u002FSpring.2019\u002Fwww) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLp-0K3kfddPzNdZPX4p0lVi6AcDXBofuf) | S2019 |\n| 42. | **Introduction to Deep Learning** | Bhiksha Raj and many others, CMU | [11-785](https:\u002F\u002Fwww.cs.cmu.edu\u002F~bhiksha\u002Fcourses\u002Fdeeplearning\u002FFall.2019\u002Fwww) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLp-0K3kfddPwz13VqV1PaMXF6V6dYdEsj) \u003Cbr> [Recitations](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLp-0K3kfddPxf4T59JEQKv5UanLPVsxzz) | F2019 |\n| 43. | **UvA Deep Learning** | Efstratios Gavves, University of Amsterdam | [UvA-DLC](https:\u002F\u002Fuvadlc.github.io\u002F) | [Lecture-Videos](https:\u002F\u002Fuvadlc.github.io\u002Flectures-apr2019.html) | S2019 |\n| 44. | **Deep Learning** | Prabir Kumar Biswas, IIT Kgp | `None` | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLbRMhDVUMngc7NM-gDwcBzIYZNFSK2N1a) | 2019 |\n| 45. | **Deep Learning and its Applications** | Aditya Nigam, IIT Mandi | [CS-671](http:\u002F\u002Ffaculty.iitmandi.ac.in\u002F~aditya\u002Fcs671\u002Findex.html) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLKvX2d3IUq586Ic9gIhZj6ubpWV-OJfl4) | 2019 |\n| 46. | **Neural Networks** | Neil Rhodes, Harvey Mudd College | [CS-152](https:\u002F\u002Fwww.cs.hmc.edu\u002F~rhodes\u002Fcs152\u002Fschedule.html) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLgEuVSRbAI9UIQSHGy4l01laA_12YOqEj) | F2019 |\n| 47. | **Deep Learning** | Thomas Hofmann, ETH Zürich | [DAL-DL](http:\u002F\u002Fwww.da.inf.ethz.ch\u002Fteaching\u002F2019\u002FDeepLearning) | [Lecture-Videos](https:\u002F\u002Fvideo.ethz.ch\u002Flectures\u002Fd-infk\u002F2019\u002Fautumn\u002F263-3210-00L.html) | F2019 |\n| 48. | **Deep Learning** | Milan Straka, Charles University | [NPFL114](https:\u002F\u002Fufal.mff.cuni.cz\u002Fcourses\u002Fnpfl114) | [Lecture-Videos](https:\u002F\u002Fufal.mff.cuni.cz\u002Fcourses\u002Fnpfl114\u002F1718-summer) | S2019 |\n| 49. | **UvA Deep Learning** | Efstratios Gavves, University of Amsterdam | [UvA-DLC-19](https:\u002F\u002Fuvadlc.github.io\u002F#lectures) | [Lecture-Videos](https:\u002F\u002Fuvadlc.github.io\u002F#lectures) | F2019 |\n| 50. | **Artificial Intelligence: Principles and Techniques** | Percy Liang and Dorsa Sadigh, Stanford University | [CS221](https:\u002F\u002Fstanford-cs221.github.io\u002Fautumn2019\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLoROMvodv4rO1NB9TD4iUZ3qghGEGtqNX) | F2019 |\n| | | | | | |\n| 51. | **Analyses of Deep Learning** | Lots of Legends, Stanford University | [STATS-385](https:\u002F\u002Fstats385.github.io\u002F) | [YouTube-Lectures](https:\u002F\u002Fstats385.github.io\u002Flecture_videos) | 2017-2019 |\n| 52. | **Deep Learning Foundations and Applications** | Debdoot Sheet and Sudeshna Sarkar, IIT-Kgp | [AI61002](http:\u002F\u002Fwww.facweb.iitkgp.ac.in\u002F~debdoot\u002Fcourses\u002FAI61002\u002FSpr2020) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL_AdDfjIMo6pZfwjZ0rJlkE_MIsmRW7Mh) | S2020 |\n| 53. | **Designing, Visualizing, and Understanding Deep Neural Networks** | John Canny, UC Berkeley | [CS 182\u002F282A](https:\u002F\u002Fbcourses.berkeley.edu\u002Fcourses\u002F1487769\u002Fpages\u002Fcs-l-w-182-slash-282a-designing-visualizing-and-understanding-deep-neural-networks-spring-2020) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLkFD6_40KJIwaO6Eca8kzsEFBob0nFvwm) | S2020 |\n| 54. | **Deep Learning** | Yann LeCun and Alfredo Canziani, NYU | [DS-GA 1008](https:\u002F\u002Fatcold.github.io\u002Fpytorch-Deep-Learning\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLLHTzKZzVU9eaEyErdV26ikyolxOsz6mq) | S2020 |\n| 55. | **Introduction to Deep Learning** | Bhiksha Raj, CMU | [11-785](https:\u002F\u002Fdeeplearning.cs.cmu.edu\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLp-0K3kfddPzCnS4CqKphh-zT3aDwybDe) | S2020 |\n| 56. | **Deep Unsupervised Learning** | Pieter Abbeel, UC Berkeley | [CS294-158](https:\u002F\u002Fsites.google.com\u002Fview\u002Fberkeley-cs294-158-sp20) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLwRJQ4m4UJjPiJP3691u-qWwPGVKzSlNP) | S2020 |\n| 57. | **Machine Learning** | Peter Bloem, Vrije Universiteit Amsterdam | [VUML](https:\u002F\u002Fmlvu.github.io\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLCof9EqayQgthR7IViXkAkUwel_rhxGYM) | S2020 |\n| 58. | **Deep Learning (with PyTorch)** | Alfredo Canziani and Yann LeCun, NYU | [DS-GA 1008](https:\u002F\u002Fatcold.github.io\u002Fpytorch-Deep-Learning\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLLHTzKZzVU9eaEyErdV26ikyolxOsz6mq) | S2020 |\n| 59. | **Introduction to Deep Learning and Generative Models** | Sebastian Raschka, UW-Madison | [Stat453](http:\u002F\u002Fpages.stat.wisc.edu\u002F~sraschka\u002Fteaching\u002Fstat453-ss2020\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLTKMiZHVd_2JkR6QtQEnml7swCnFBtq4P) | S2020 |\n| 60. | **Deep Learning** | Andreas Maier, FAU Erlangen-Nürnberg | [DL-2020](https:\u002F\u002Fwww.video.uni-erlangen.de\u002Fcourse\u002Fid\u002F925) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLpOGQvPCDQzvgpD3S0vTy7bJe2pf_yJFj) \u003Cbr\u002F>[Lecture-Videos](https:\u002F\u002Fwww.video.uni-erlangen.de\u002Fcourse\u002Fid\u002F925) | SS2020 |\n| | | | | | |\n| 61. | **Introduction to Deep Learning** | Laura Leal-Taixé and Matthias Niessner, TU-München | [I2DL-IN2346](https:\u002F\u002Fdvl.in.tum.de\u002Fteaching\u002Fi2dl-ss20\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLQ8Y4kIIbzy_OaXv86lfbQwPHSomk2o2e) | SS2020 |\n| 62. | **Deep Learning** | Sargur Srihari, SUNY-Buffalo | [CSE676](https:\u002F\u002Fcedar.buffalo.edu\u002F~srihari\u002FCSE676\u002F) | [YouTube-Lectures-P1](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLmx4utxjUQD70k_NzeiSIXf30m54T_e1h) \u003Cbr\u002F>[YouTube-Lectures-P2](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCUm7yUmVJyAbYh_0ppJ4H-g\u002Fvideos) | 2020 |\n| 63. | **Deep Learning Lecture Series** | Lots of Legends, DeepMind x UCL, London | [DLLS-20](https:\u002F\u002Fdeepmind.com\u002Flearning-resources\u002Fdeep-learning-lecture-series-2020) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLqYmG7hTraZCDxZ44o4p3N5Anz3lLRVZF) | 2020 |\n| 64. | **MultiModal Machine Learning** | Louis-Philippe Morency & others, Carnegie Mellon University | [11-777 MMML-20](https:\u002F\u002Fcmu-multicomp-lab.github.io\u002Fmmml-course\u002Ffall2020) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCqlHIJTGYhiwQpNuPU5e2gg\u002Fvideos) | F2020 |\n| 65. | **Reliable and Interpretable Artificial Intelligence** | Martin Vechev, ETH Zürich | [RIAI-20](https:\u002F\u002Fwww.sri.inf.ethz.ch\u002Fteaching\u002Friai2020) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLWjm4hHpaNg6c-W7JjNYDEC_kJK9oSp0Y) | F2020 |\n| 66. | **Fundamentals of Deep Learning** | David McAllester, Toyota Technological Institute, Chicago | [TTIC-31230](https:\u002F\u002Fmcallester.github.io\u002Fttic-31230\u002FFall2020) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCciVrtrRR3bQdaGbti9-hVQ\u002Fvideos) | F2020 |\n| 67. | **Foundations of Deep Learning** | Soheil Feize, University of Maryland, College Park | [CMSC 828W](http:\u002F\u002Fwww.cs.umd.edu\u002Fclass\u002Ffall2020\u002Fcmsc828W) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLHgjs9ncvHi80UCSlSvQe-TK_uOyDv_Jf) | F2020 |\n| 68. | **Deep Learning** | Andreas Geiger, Universität Tübingen | [DL-UT](https:\u002F\u002Funi-tuebingen.de\u002Ffakultaeten\u002Fmathematisch-naturwissenschaftliche-fakultaet\u002Ffachbereiche\u002Finformatik\u002Flehrstuehle\u002Fautonomous-vision\u002Fteaching\u002Flecture-deep-learning\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL05umP7R6ij3NTWIdtMbfvX7Z-4WEXRqD) | W20\u002F21 |\n| 69. | **Deep Learning** | Andreas Maier, FAU Erlangen-Nürnberg | [DL-FAU](https:\u002F\u002Fwww.fau.tv\u002Fcourse\u002Fid\u002F1599) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLpOGQvPCDQzvJEPFUQ3mJz72GJ95jyZTh) | W20\u002F21 |\n| 70. | **Fundamentals of Deep Learning** | Terence Parr and Yannet Interian, University of San Francisco | [DL-Fundamentals](https:\u002F\u002Fgithub.com\u002Fparrt\u002Ffundamentals-of-deep-learning) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLFCc_Fc116ikeol9CZcWWKqmrJljxhE4N) | S2021 |\n| | | | | | |\n| 71. | **Full Stack Deep Learning** | Pieter Abbeel, Sergey Karayev, UC Berkeley | [FS-DL](https:\u002F\u002Ffullstackdeeplearning.com\u002Fspring2021) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL1T8fO7ArWlcWg04OgNiJy91PywMKT2lv) | S2021 |\n| 72. | **Deep Learning: Designing, Visualizing, and Understanding DNNs** | Sergey Levine, UC Berkeley | [CS 182](https:\u002F\u002Fcs182sp21.github.io) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL_iWQOsE6TfVmKkQHucjPAoRtIJYt8a5A) | S2021 |\n| 73. | **Deep Learning in the Life Sciences** | Manolis Kellis, MIT | [6.874](https:\u002F\u002Fmit6874.github.io) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLypiXJdtIca5sxV7aE3-PS9fYX3vUdIOX) | S2021 |\n| 74. | **Introduction to Deep Learning and Generative Models** | Sebastian Raschka, University of Wisconsin-Madison | [Stat 453](http:\u002F\u002Fpages.stat.wisc.edu\u002F~sraschka\u002Fteaching\u002Fstat453-ss2021) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLTKMiZHVd_2KJtIXOW0zFhFfBaJJilH51) | S2021 |\n| 75. | **Deep Learning** | Alfredo Canziani and Yann LeCun, NYU | [NYU-DLSP21](https:\u002F\u002Fatcold.github.io\u002FNYU-DLSP21) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLLHTzKZzVU9e6xUfG10TkTWApKSZCzuBI) | S2021 |\n| 76. | **Applied Deep Learning** | Alexander Pacha, TU Wien | `None` | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLNsFwZQ_pkE8xNYTEyorbaWPN7nvbWyk1) | 2020-2021 |\n| 77. | **Machine Learning** | Hung-yi Lee, National Taiwan University | [ML'21](https:\u002F\u002Fspeech.ee.ntu.edu.tw\u002F~hylee\u002Fml\u002F2021-spring.php) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLJV_el3uVTsNxV_IGauQZBHjBKZ26JHjd) | S2021 |\n| 78. | **Mathematics of Deep Learning** | Lots of legends, FAU | [MoDL](https:\u002F\u002Fwww.fau.tv\u002Fcourse\u002Fid\u002F878) | [Lecture-Videos](https:\u002F\u002Fwww.fau.tv\u002Fcourse\u002Fid\u002F878) | 2019-21 |\n| 79. | **Deep Learning** | Peter Bloem, Michael Cochez, and Jakub Tomczak, VU-Amsterdam | [DL](https:\u002F\u002Fdlvu.github.io\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCYh1zKnwzrSjrO2Ae-akfTg\u002Fplaylists) | 2020-21 |\n| 80. | **Applied Deep Learning** | Maziar Raissi, UC Boulder | [ADL'21](https:\u002F\u002Fgithub.com\u002Fmaziarraissi\u002FApplied-Deep-Learning) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLoEMreTa9CNmuxQeIKWaz7AVFd_ZeAcy4) | 2021 |\n| | | | | | |\n| 81. | **An Introduction to Group Equivariant Deep Learning** | Erik J. Bekkers, Universiteit van Amsterdam | [UvAGEDL](https:\u002F\u002Fuvagedl.github.io) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL8FnQMH2k7jzPrxqdYufoiYVHim8PyZWd) | 2022 |\n| | | | | | |\n\n[Go to Contents :arrow_heading_up:](https:\u002F\u002Fgithub.com\u002Fkmario23\u002Fdeep-learning-drizzle#contents) \n\n:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:\n### :cupid: Machine Learning Fundamentals :cyclone: :boom: \n:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:\n\n| S.No | Course Name | University\u002FInstructor(s) | Course Webpage | Video Lectures | Year |\n| ---- | ------------------------------------------------------------ | ------------------------------------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ---------- |\n| 1. | **Linear Algebra** | Gilbert Strang, MIT | [18.06 SC](http:\u002F\u002Focw.mit.edu\u002F18-06SCF11) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL221E2BBF13BECF6C) | 2011 |\n| 2. | **Probability Primer** | Jeffrey Miller, Brown University | `mathematical monk` | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL17567A1A3F5DB5E4) | 2011 |\n| 3. | **Information Theory, Pattern Recognition, and Neural Networks** | David Mackay, University of Cambridge | [ITPRNN](http:\u002F\u002Fwww.inference.org.uk\u002Fmackay\u002Fitprnn) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLruBu5BI5n4aFpG32iMbdWoRVAA-Vcso6) | 2012 |\n| 4. | **Linear Algebra Review** | Zico Kolter, CMU | [LinAlg](http:\u002F\u002Fwww.cs.cmu.edu\u002F~zkolter\u002Fcourse\u002Flinalg\u002Findex.html) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLM4Pv4KYYzGzL5ay6dmpyzRnbzQ__8v_t) | 2013 |\n| 5. | **Probability and Statistics** | Michel van Biezen | `None` | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLX2gX-ftPVXUWwTzAkOhBdhplvz0fByqV) | 2015 |\n| 6. | **Linear Algebra: An in-depth Introduction** | Pavel Grinfeld | `None` | [Part-1](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLlXfTHzgMRUKXD88IdzS14F4NxAZudSmv) \u003Cbr\u002F> [Part-2](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLlXfTHzgMRULWJYthculb2QWEiZOkwTSU) \u003Cbr\u002F> [Part-3](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLlXfTHzgMRUIqYrutsFXCOmiqKUgOgGJ5) \u003Cbr\u002F> [Part-4](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLlXfTHzgMRULZfrNCrrJ7xDcTjGr633mm) | 2015- 2017 |\n| 7. | **Multivariable Calculus** | Grant Sanderson, Khan Academy | `None` | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLSQl0a2vh4HC5feHa6Rc5c0wbRTx56nF7) | 2016 |\n| 8. | **Essence of Linear Algebra** | Grant Sanderson | `None` | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab) | 2016 |\n| 9. | **Essence of Calculus** | Grant Sanderson | `None` | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLZHQObOWTQDMsr9K-rj53DwVRMYO3t5Yr) | 2017-2018 |\n| 10. | **Math Background for Machine Learning** | Geoff Gordon, CMU | [10-606](https:\u002F\u002Fcanvas.cmu.edu\u002Fcourses\u002F603\u002Fassignments\u002Fsyllabus), [10-607](https:\u002F\u002Fpiazza.com\u002Fcmu\u002Ffall2017\u002F1060610607\u002Fhome) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL7y-1rk2cCsAqRtWoZ95z-GMcecVG5mzA) | F2017 |\n| | | | | | |\n| 11. | **Mathematics for Machine Learning** (Linear Algebra, Calculus) | David Dye, Samuel Cooper, and Freddie Page, IC-London | [MML](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Flinear-algebra-machine-learning) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLmAuaUS7wSOP-iTNDivR0ANKuTUhEzMe4) | 2018 |\n| 12. | **Multivariable Calculus** | S.K. Gupta and Sanjeev Kumar, IIT-Roorkee | [MVC](https:\u002F\u002Fnptel.ac.in\u002Fsyllabus\u002F111107108\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLq-Gm0yRYwTiQtK374NzhFOcQkWmJ71vx) | 2018 |\n| 13. | **Engineering Probability** | Rich Radke, Rensselaer Polytechnic Institute | `None` | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLuh62Q4Sv7BU1dN2G6ncyiMbML7OXh_Jx) | 2018 |\n| 14. | **Matrix Methods in Data Analysis, Signal Processing, and Machine Learning** | Gilbert Strang, MIT | [18.065](https:\u002F\u002Focw.mit.edu\u002Fcourses\u002Fmathematics\u002F18-065-matrix-methods-in-data-analysis-signal-processing-and-machine-learning-spring-2018) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLUl4u3cNGP63oMNUHXqIUcrkS2PivhN3k) | S2018 |\n| 15. | **Information Theory** | Himanshu Tyagi, IISC, Bengaluru | [E2 201](https:\u002F\u002Fece.iisc.ac.in\u002F~htyagi\u002Fcourse-E2201-2020.html) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLgMDNELGJ1CYS-8dlMGPIaowVfeda4nUj) | 2018-20 |\n| 16. | **Math Camp** | Mark Walker, University of Arizona | [UAMathCamp \u002F Econ-519](http:\u002F\u002Fwww.u.arizona.edu\u002F~mwalker\u002FMathCamp2019.htm) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLcjqUUQt__ZGLhwUacPm7_RKs2eJNFwco) | 2019 |\n| 17. | **A 2020 Vision of Linear Algebra** | Gilbert Strang, MIT | [VoLA](https:\u002F\u002Focw.mit.edu\u002Fresources\u002Fres-18-010-a-2020-vision-of-linear-algebra-spring-2020\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLUl4u3cNGP61iQEFiWLE21EJCxwmWvvek) | S2020 |\n| 18. | **Mathematics for Numerical Computing and Machine Learning** | Szymon Rusinkiewicz, Princeton University | [COS-302](https:\u002F\u002Fwww.cs.princeton.edu\u002Fcourses\u002Farchive\u002Ffall20\u002Fcos302\u002Foutline.html) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL88aSuXxl_dSjC5pIG8bGkC5wsUPyW_Hh) | F2020 |\n| 19. | **Essential Statistics for Neuroscientists** | Philipp Berens, Universität Klinikum Tübingen | `None` | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL05umP7R6ij0Gw5SLIrOA1dMYScCx4oXT) | 2020 |\n| 20. | **Mathematics for Machine Learning** | Ulrike von Luxburg, Eberhard Karls Universität Tübingen | [Math4ML](https:\u002F\u002Fwww.tml.cs.uni-tuebingen.de\u002Fteaching\u002F2020_maths_for_ml) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL05umP7R6ij1a6KdEy8PVE9zoCv6SlHRS) | W2020 |\n| 21. | **Introduction to Causal Inference** | Brady Neal, Mila, Montréal | [CausalInf](https:\u002F\u002Fwww.bradyneal.com\u002Fcausal-inference-course) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLoazKTcS0Rzb6bb9L508cyJ1z-U9iWkA0) | F2020 |\n| 22. | **Applied Linear Algebra** | Andrew Thangaraj, IIT Madras | [EE5120](http:\u002F\u002Fwww.ee.iitm.ac.in\u002F~andrew\u002FEE5120) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLyqSpQzTE6M-CHZU5RGfamcXOnuFyTOpm) | 2021 |\n| 23. | **Mathematical Tools for Data Science** | Carlos Fernandez-Granda, New York University | [DS-GA 1013\u002FMath-GA 2824](https:\u002F\u002Fcds.nyu.edu\u002Fmath-tools) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLBEf5mJtE6KtU6YlXFZD6lyYcHhW5pIlc) | 2021 |\n| 24. | **Mathematics for Numerical Computing and Machine Learning** | Ryan Adams, Princeton University | [COS 302 \u002F SML 305](https:\u002F\u002Fwww.cs.princeton.edu\u002Fcourses\u002Farchive\u002Fspring21\u002Fcos302) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLCO4cUaBLHFEHo42HVIVWaSOvbAiH30uc) | 2021 |\n| | | | | | |\n| | | | | | |\n\n[Go to Contents :arrow_heading_up:](https:\u002F\u002Fgithub.com\u002Fkmario23\u002Fdeep-learning-drizzle#contents) \n\n:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:\n### :cupid: Optimization for Machine Learning :cyclone: :boom: \n:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:\n\n| S.No | Course Name | University\u002FInstructor(s) | Course Webpage | Video Lectures | Year |\n| ---- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | ---------- |\n| 1. | **Convex Optimization** | Stephen Boyd, Stanford University | [ee364a](http:\u002F\u002Fweb.stanford.edu\u002Fclass\u002Fee364a\u002Flectures.html) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL3940DD956CDF0622) | 2008 |\n| 2. | **Introduction to Optimization** | Michael Zibulevsky, Technion | [CS-236330](https:\u002F\u002Fsites.google.com\u002Fsite\u002Fmichaelzibulevsky\u002Foptimization-course) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLDFB2EEF4DDAFE30B) | 2009 |\n| 3. | **Optimization for Machine Learning** | S V N Vishwanathan, Purdue University | `None` | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL09B0E8AFC69BE108) | 2011 |\n| 4. | **Optimization** | Geoff Gordon & Ryan Tibshirani, CMU | [10-725](https:\u002F\u002Fwww.cs.cmu.edu\u002F~ggordon\u002F10725-F12\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL7y-1rk2cCsDOv91McLOnV4kExFfTB7dU) | 2012 |\n| 5. | **Convex Optimization** | Joydeep Dutta, IIT-Kanpur | [cvx-nptel](https:\u002F\u002Fnptel.ac.in\u002Fcourses\u002F111\u002F104\u002F111104068) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLbMVogVj5nJQHFqfiSdgaLCCWvDcm1W4l) | 2013 |\n| 6. | **Foundations of Optimization** | Joydeep Dutta, IIT-Kanpur | [fop-nptel](https:\u002F\u002Fnptel.ac.in\u002Fcourses\u002F111\u002F104\u002F111104071) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLbMVogVj5nJRRbofh3Qm3P6_NVyevDGD_) | 2014 |\n| 7. | **Algorithmic Aspects of Machine Learning** | Ankur Moitra, MIT | [18.409-AAML](http:\u002F\u002Fpeople.csail.mit.edu\u002Fmoitra\u002F409.html) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLB3sDpSRdrOvI1hYXNsa6Lety7K8FhPpx) | S2015 |\n| 8. | **Numerical Optimization** | Shirish K. Shevade, IISC | `None` | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL6EA0722B99332589) | 2015 |\n| 9. | **Convex Optimization** | Ryan Tibshirani, CMU | [10-725](https:\u002F\u002Fwww.stat.cmu.edu\u002F~ryantibs\u002Fconvexopt-S15\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLjbUi5mgii6BZBhJ9nW7eydgycyCOYeZ6) | S2015 |\n| 10. | **Convex Optimization** | Ryan Tibshirani, CMU | [10-725](http:\u002F\u002Fstat.cmu.edu\u002F~ryantibs\u002Fconvexopt-F15\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLjbUi5mgii6AGJW3La3BpEXe27n8v3biT) | F2015 |\n| 11. | **Advanced Algorithms** | Ankur Moitra, MIT | [6.854-AA](http:\u002F\u002Fpeople.csail.mit.edu\u002Fmoitra\u002F854.html) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL6ogFv-ieghdoGKGg2Bik3Gl1glBTEu8c) | S2016 |\n| 12. | **Introduction to Optimization** | Michael Zibulevsky, Technion | `None` | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLBD31626529B0AC2A) | 2016 |\n| 13. | **Convex Optimization** | Javier Peña & Ryan Tibshirani | [10-725\u002F36-725](https:\u002F\u002Fwww.stat.cmu.edu\u002F~ryantibs\u002Fconvexopt-F16) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLjbUi5mgii6AVdvImLB9-Hako68p9MpIC) | F2016 |\n| 14. | **Convex Optimization** | Ryan Tibshirani, CMU | [10-725](https:\u002F\u002Fwww.stat.cmu.edu\u002F~ryantibs\u002Fconvexopt-F18\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLpIxOj-HnDsMM7BCNGC3hPFU3DfCWfVIw) \u003Cbr\u002F> [Lecture-Videos](https:\u002F\u002Fwww.stat.cmu.edu\u002F~ryantibs\u002Fconvexopt-F18\u002F) | F2018 |\n| 15. | **Modern Algorithmic Optimization** | Yurii Nesterov, UCLouvain | `None` | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLEqoHzpnmTfAoUDqnmMly-KgyJ6ZM_axf) | 2018 |\n| 16. | **Optimization, Foundations of Optimization** | Mark Walker, University of Arizona | [MathCamp-20](http:\u002F\u002Fwww.u.arizona.edu\u002F~mwalker\u002FMathCamp2020\u002FMathCamp2020LectureNotes.htm) | [YouTube-Lectures-Found.](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLcjqUUQt__ZE6wp_c4-FcRdmzBvx8VN7O) \u003Cbr\u002F> [YouTube-Lectures-Opt](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLcjqUUQt__ZE0ZSTNRyBIgLJ5obPHdmxC) | 2019 - now |\n| 17. | **Optimization: Principles and Algorithms** | Michel Bierlaire, École polytechnique fédérale de Lausanne (EPFL) | [opt-algo](https:\u002F\u002Ftransp-or.epfl.ch\u002Fbooks\u002Foptimization\u002Fhtml\u002Fabout_book.html) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLM4Pv4KYYzGzOpWwsaV6GgllT6njsi1G-) | 2019 |\n| 18. | **Optimization and Simulation** | Michel Bierlaire, École polytechnique fédérale de Lausanne (EPFL) | [opt-sim](https:\u002F\u002Ftransp-or.epfl.ch\u002Fcourses\u002FOptSim2019\u002Fslides.php) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL10NOnsbP5Q5NlJ-Y6Eiup6RTSfkuj1TR) | S2019 |\n| 19. | **Brazilian Workshop on Continuous Optimization** | Lots of Legends, Instituto Nacional de Matemática Pura e Aplicada, Rio de Janeiro | [cont. opt.](https:\u002F\u002Fimpa.br\u002Feventos-do-impa\u002Feventos-2019\u002Fxiii-brazilian-workshop-on-continuous-optimization) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLo4jXE-LdDTQVZhnLPq2W31vJ1fq1VSp6) | 2019 |\n| 20. | **One World Optimization Seminar** | Lots of Legends, Universität Wien | [1W-OPT](https:\u002F\u002Fowos.univie.ac.at) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLBQo-yZOMzLWEcAptzTYOnwXo9hhXrAa2) | 2020- |\n| | | | | | |\n| 21. | **Convex Optimization II** | Constantine Caramanis, UT Austin | [CVX-Optim-II](http:\u002F\u002Fusers.ece.utexas.edu\u002F~cmcaram\u002Fconstantine_caramanis\u002FAnnouncements.html) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLXsmhnDvpjORzPelSDs0LSDrfJcqyLlZc) | S2020 |\n| 22. | **Combinatorial Optimization** | Constantine Caramanis, UT Austin | [comb-op](https:\u002F\u002Fcaramanis.github.io\u002Fteaching\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLXsmhnDvpjORcTRFMVF3aUgyYlHsxfhNL) | F2020 |\n| 23. | **Optimization Methods for Machine Learning and Engineering** | Julius Pfrommer, Jürgen Beyerer, Karlsruher Institut für Technologie (KIT) | [Optim-MLE](https:\u002F\u002Fies.iar.kit.edu\u002Flehre_1487.php), [slides](https:\u002F\u002Fdrive.google.com\u002Fdrive\u002Ffolders\u002F1WWVWV4vDBIOkjZc6uFY3nfXvpaOUHcfb) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLdkTDauaUnQpzuOCZyUUZc0lxf4-PXNR5) | W2020-21 |\n| | | | | | |\n\n[Go to Contents :arrow_heading_up:](https:\u002F\u002Fgithub.com\u002Fkmario23\u002Fdeep-learning-drizzle#contents) \n\n:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:\n### :cupid: General Machine Learning :cyclone: :boom: \n:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:\n\n| S.No | Course Name | University\u002FInstructor(s) | Course Webpage | Video Lectures | Year |\n| ---- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | --------- |\n| 1. | **CS229: Machine Learning** | Andrew Ng, Stanford University | [CS229-old](https:\u002F\u002Fsee.stanford.edu\u002FCourse\u002FCS229\u002F) \u003Cbr\u002F> [CS229-new](http:\u002F\u002Fcs229.stanford.edu\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLA89DCFA6ADACE599) | 2007 |\n| 2. | **Machine Learning** | Jeffrey Miller, Brown University | `mathematical monk` | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLD0F06AA0D2E8FFBA) | 2011 |\n| 3. | **Machine Learning** | Tom Mitchell, CMU | [10-701](http:\u002F\u002Fwww.cs.cmu.edu\u002F~tom\u002F10701_sp11\u002F) | [Lecture-Videos](http:\u002F\u002Fwww.cs.cmu.edu\u002F~tom\u002F10701_sp11\u002Flectures.shtml) | 2011 |\n| 4. | **Machine Learning and Data Mining** | Nando de Freitas, University of British Columbia | [CPSC-340](https:\u002F\u002Fwww.cs.ubc.ca\u002F~nando\u002F340-2012\u002Findex.php) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLE6Wd9FR--Ecf_5nCbnSQMHqORpiChfJf) | 2012 |\n| 5. | **Learning from Data** | Yaser Abu-Mostafa, CalTech | [CS156](http:\u002F\u002Fwork.caltech.edu\u002Ftelecourse.html) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLD63A284B7615313A) | 2012 |\n| 6. | **Machine Learning** | Rudolph Triebel, Technische Universität München | [Machine Learning](https:\u002F\u002Fvision.in.tum.de\u002Fteaching\u002Fws2013\u002Fml_ws13) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLTBdjV_4f-EIiongKlS9OKrBEp8QR47Wl) | 2013 |\n| 7. | **Introduction to Machine Learning** | Alex Smola, CMU | [10-701](http:\u002F\u002Falex.smola.org\u002Fteaching\u002Fcmu2013-10-701\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLZSO_6-bSqHQmMKwWVvYwKreGu4b4kMU9) | 2013 |\n| 8. | **Introduction to Machine Learning** | Alex Smola and Geoffrey Gordon, CMU | [10-701x](http:\u002F\u002Falex.smola.org\u002Fteaching\u002Fcmu2013-10-701x\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLZSO_6-bSqHR7NPk4k0zqdm2dPdraQZ_B) | 2013 |\n| 9. | **Pattern Recognition** | Sukhendu Das, IIT-M and C.A. Murthy, ISI-Calcutta | [PR-NPTEL](https:\u002F\u002Fnptel.ac.in\u002Fsyllabus\u002F106106046\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLbMVogVj5nJQJMLb2CYw9rry0d5s0TQRp) | 2014 |\n| 10. | **An Introduction to Statistical Learning with Applications in R** | Trevor Hastie and Robert Tibshirani, Stanford | [stat-learn](https:\u002F\u002Flagunita.stanford.edu\u002Fcourses\u002FHumanitiesandScience\u002FStatLearning\u002FWinter2015\u002Fabout) \u003Cbr\u002F> [R-bloggers](https:\u002F\u002Fwww.r-bloggers.com\u002Fin-depth-introduction-to-machine-learning-in-15-hours-of-expert-videos\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLOg0ngHtcqbPTlZzRHA2ocQZqB1D_qZ5V) | 2014 |\n| | | | | | |\n| 11. | **Introduction to Machine Learning** | Katie Malone, Sebastian Thrun, Udacity | [ML-Udacity](https:\u002F\u002Fwww.udacity.com\u002Fcourse\u002Fud120) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLAwxTw4SYaPkQXg8TkVdIvYv4HfLG7SiH) | 2015 |\n| 12. | **Introduction to Machine Learning** | Dhruv Batra, Virginia Tech | [ECE-5984](https:\u002F\u002Ffilebox.ece.vt.edu\u002F~s15ece5984\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL-fZD610i7yDUiNTFy-tEOxkTwg4mHZHu) | 2015 |\n| 13. | **Statistical Learning - Classification** | Ali Ghodsi, University of Waterloo | [STAT-441](https:\u002F\u002Fuwaterloo.ca\u002Fdata-analytics\u002Fstatistical-learning-classification) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLehuLRPyt1Hy-4ObWBK4Ab0xk97s6imfC) | 2015 |\n| 14. | **Machine Learning Theory** | Shai Ben-David, University of Waterloo | `None` | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLPW2keNyw-usgvmR7FTQ3ZRjfLs5jT4BO) | 2015 |\n| 15. | **Introduction to Machine Learning** | Alex Smola, CMU | [10-701](http:\u002F\u002Falex.smola.org\u002Fteaching\u002F10-701-15\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLZSO_6-bSqHTTV7w9u7grTXBHMH-mw3qn) | S2015 |\n| 16. | **Statistical Machine Learning** | Larry Wasserman, CMU | `None` | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLjbUi5mgii6BWEUZf7He6nowWvGne_Y8r) | S2015 |\n| 17. | **ML: Supervised Learning** | Michael Littman, Charles Isbell, Pushkar Kolhe, GaTech | [ML-Udacity](https:\u002F\u002Feu.udacity.com\u002Fcourse\u002Fmachine-learning--ud262) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLAwxTw4SYaPl0N6-e1GvyLp5-MUMUjOKo) | 2015 |\n| 18. | **ML: Unsupervised Learning** | Michael Littman, Charles Isbell, Pushkar Kolhe, GaTech | [ML-Udacity](https:\u002F\u002Feu.udacity.com\u002Fcourse\u002Fmachine-learning-unsupervised-learning--ud741) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLAwxTw4SYaPmaHhu-Lz3mhLSj-YH-JnG7) | 2015 |\n| 19. | **Advanced Introduction to Machine Learning** | Barnabas Poczos and Alex Smola | [10-715](https:\u002F\u002Fwww.cs.cmu.edu\u002F~bapoczos\u002FClasses\u002FML10715_2015Fall\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL4YhK0pT0ZhWBzSBkMGzpnPw6sf6Ma0IX) | F2015 |\n| 20. | **Machine Learning** | Pedro Domingos, UWashington | [CSEP-546](https:\u002F\u002Fcourses.cs.washington.edu\u002Fcourses\u002Fcsep546\u002F16sp\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLTPQEx-31JXgtDaC6-3HxWcp7fq4N8YGr) | S2016 |\n| | | | | | |\n| 21. | **Statistical Machine Learning** | Larry Wasserman, CMU | `None` | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLTB9VQq8WiaCBK2XrtYn5t9uuPdsNm7YE) | S2016 |\n| 22. | **Machine Learning with Large Datasets** | William Cohen, CMU | [10-605](http:\u002F\u002Fcurtis.ml.cmu.edu\u002Fw\u002Fcourses\u002Findex.php\u002FMachine_Learning_with_Large_Datasets_10-605_in_Fall_2016) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLnfBqXRW5MRhPtfkadfwQ0VcuSi2IwEcW) | F2016 |\n| 23. | **Math Background for Machine Learning** | Geoffrey Gordon, CMU | `10-600` | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL7y-1rk2cCsA339crwXMWUaBRuLBvPBCg) | F2016 |\n| 24. | **Statistical Learning - Classification** | Ali Ghodsi, University of Waterloo | `None` | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLehuLRPyt1HzXDemu7K4ETcF0Ld_B5adG) | 2017 |\n| 25. | **Machine Learning** | Andrew Ng, Stanford University | [Coursera-ML](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Fmachine-learning) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLLssT5z_DsK-h9vYZkQkYNWcItqhlRJLN) | 2017 |\n| 26. | **Machine Learning** | Roni Rosenfield, CMU | [10-601](http:\u002F\u002Fwww.cs.cmu.edu\u002F~roni\u002F10601-f17\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL7k0r4t5c10-g7CWCnHfZOAxLaiNinChk) | 2017 |\n| 27. | **Statistical Machine Learning** | Ryan Tibshirani, Larry Wasserman, CMU | [10-702](http:\u002F\u002Fwww.stat.cmu.edu\u002F~ryantibs\u002Fstatml\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLjbUi5mgii6B7A0nM74zHTOVQtTC9DaCv) | S2017 |\n| 28. | **Machine Learning for Computer Vision** | Fred Hamprecht, Heidelberg University | `None` | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLuRaSnb3n4kSQFyt8VBldsQ9pO9Xtu8rY) | F2017 |\n| 29. | **Math Background for Machine Learning** | Geoffrey Gordon, CMU | [10-606 \u002F 10-607](https:\u002F\u002Fcanvas.cmu.edu\u002Fcourses\u002F603\u002Fassignments\u002Fsyllabus) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL7y-1rk2cCsAqRtWoZ95z-GMcecVG5mzA) | F2017 |\n| 30. | **Data Visualization** | Ali Ghodsi, University of Waterloo | `None` | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLehuLRPyt1HzQoXEhtNuYTmd0aNQvtyAK) | 2017 |\n| | | | | | |\n| 31. | **Machine Learning for Physicists** | Florian Marquardt, Uni Erlangen-Nürnberg | [ML4Phy-17](http:\u002F\u002Fwww.thp2.nat.uni-erlangen.de\u002Findex.php\u002F2017_Machine_Learning_for_Physicists,_by_Florian_Marquardt) | [Lecture-Videos](https:\u002F\u002Fwww.video.uni-erlangen.de\u002Fcourse\u002Fid\u002F574) | 2017 |\n| 32. | **Machine Learning for Intelligent Systems** | Kilian Weinberger, Cornell University | [CS4780](http:\u002F\u002Fwww.cs.cornell.edu\u002Fcourses\u002Fcs4780\u002F2018fa\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLl8OlHZGYOQ7bkVbuRthEsaLr7bONzbXS) | F2018 |\n| 33. | **Statistical Learning Theory and Applications** | Tomaso Poggio, Lorenzo Rosasco, Sasha Rakhlin | [9.520\u002F6.860](https:\u002F\u002Fcbmm.mit.edu\u002Flh-9-520) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLyGKBDfnk-iAtLO6oLW4swMiQGz4f2OPY) | F2018 |\n| 34. | **Machine Learning and Data Mining** | Mike Gelbart, University of British Columbia | [CPSC-340](https:\u002F\u002Fubc-cs.github.io\u002Fcpsc340\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLWmXHcz_53Q02ZLeAxigki1JZFfCO6M-b) | 2018 |\n| 35. | **Foundations of Machine Learning** | David Rosenberg, Bloomberg | [FOML](https:\u002F\u002Fbloomberg.github.io\u002Ffoml\u002F#home) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLnZuxOufsXnvftwTB1HL6mel1V32w0ThI) | 2018 |\n| 36. | **Introduction to Machine Learning** | Andreas Krause, ETH Zürich | [IntroML](https:\u002F\u002Flas.inf.ethz.ch\u002Fteaching\u002Fintroml-s18) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLzn6LN6WhlN273tsqyfdrBUsA-o5nUESV) | 2018 |\n| 37. | **Machine Learning Fundamentals** | Sanjoy Dasgupta, UC-San Diego | [MLF-slides](https:\u002F\u002Fdrive.google.com\u002Fdrive\u002Ffolders\u002F1l1rwv-jMihLZIpW0zTgGN9-snWOsA3M9) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL_onPhFCkVQhUzcTVgQiC8W2ShZKWlm0s) | 2018 |\n| 38. | **Machine Learning** | Jordan Boyd-Graber, University of Maryland | [CMSC-726](http:\u002F\u002Fusers.umiacs.umd.edu\u002F~jbg\u002Fteaching\u002FCMSC_726\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLegWUnz91WfsELyRcZ7d1GwAVifDaZmgo) | 2015-2018 |\n| 39. | **Machine Learning** | Andrew Ng, Stanford University | [CS229](http:\u002F\u002Fcs229.stanford.edu\u002Fsyllabus-autumn2018.html) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLoROMvodv4rMiGQp3WXShtMGgzqpfVfbU) | 2018 |\n| 40. | **Machine Intelligence** | H.R.Tizhoosh, UWaterloo | [SYDE-522](https:\u002F\u002Fkimialab.uwaterloo.ca\u002Fkimia\u002Findex.php\u002Fteaching\u002Fsyde-522-machine-intelligence-2) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL4upCU5bnihwCX93Gv6AQnKmVMwx4AZoT) | 2019 |\n| | | | | | |\n| 41. | **Introduction to Machine Learning** | Pascal Poupart, University of Waterloo | [CS480\u002F680](https:\u002F\u002Fcs.uwaterloo.ca\u002F~ppoupart\u002Fteaching\u002Fcs480-spring19) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLdAoL1zKcqTW-uzoSVBNEecKHsnug_M0k) | S2019 |\n| 42. | **Advanced Machine Learning** | Thorsten Joachims, Cornell University | [CS-6780](https:\u002F\u002Fwww.cs.cornell.edu\u002Fcourses\u002Fcs6780\u002F2019sp) | [Lecture-Videos](https:\u002F\u002Fcornell.mediasite.com\u002FMediasite\u002FCatalog\u002FFull\u002Ff5d1cd3323f746cca80b2468bf97efd421) | S2019 |\n| 43. | **Machine Learning for Structured Data** | Matt Gormley, Carnegie Mellon University | [10-418\u002F10-618](http:\u002F\u002Fwww.cs.cmu.edu\u002F~mgormley\u002Fcourses\u002F10418\u002Fschedule.html) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL4CxkUJbvNVihRKP4bXufvRLIWzeS-ieP) | F2019 |\n| 44. | **Advanced Machine Learning** | Joachim Buhmann, ETH Zürich | [ML2-AML](https:\u002F\u002Fml2.inf.ethz.ch\u002Fcourses\u002Faml\u002F) | [Lecture-Videos](https:\u002F\u002Fvideo.ethz.ch\u002Flectures\u002Fd-infk\u002F2019\u002Fautumn\u002F252-0535-00L.html) | F2019 |\n| 45. | **Machine Learning for Signal Processing** | Vipul Arora, IIT-Kanpur | [MLSP](http:\u002F\u002Fhome.iitk.ac.in\u002F~vipular\u002Fstuff\u002F2019_MLSP.html) | [Lecture-Videos](https:\u002F\u002Fiitk-my.sharepoint.com\u002F:f:\u002Fg\u002Fpersonal\u002Fvipular_iitk_ac_in\u002FEnf97NZfsoVBiyclC6yHfe4BlUv6CA4U8LPQQ4vtsDo_Xg) | F2019 |\n| 46. | **Foundations of Machine Learning** | Animashree Anandkumar, CalTech | [CMS-165](http:\u002F\u002Ftensorlab.cms.caltech.edu\u002Fusers\u002Fanima\u002Fcms165-2019.html) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLVNifWxslHCA5GUh0o92neMiWiQiGVFqp) | 2019 |\n| 47. | **Machine Learning for Physicists** | Florian Marquardt, Uni Erlangen-Nürnberg | `None` | [Lecture-Videos](https:\u002F\u002Fwww.video.uni-erlangen.de\u002Fcourse\u002Fid\u002F778) | 2019 |\n| 48. | **Applied Machine Learning** | Andreas Müller, Columbia University | [COMS-W4995](https:\u002F\u002Fwww.cs.columbia.edu\u002F~amueller\u002Fcomsw4995s19\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL_pVmAaAnxIQGzQS2oI3OWEPT-dpmwTfA) | 2019 |\n| 49. | **Fundamentals of Machine Learning over Networks** | Hossein Shokri-Ghadikolaei, KTH, Sweden | [MLoNs](https:\u002F\u002Fsites.google.com\u002Fview\u002Fmlons\u002Fcourse-materials) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLWoZTd81WFCEBFrxDfNUrDnt3ABdLfg80) | 2019 |\n| 50. | **Foundations of Machine Learning and Statistical Inference** | Animashree Anandkumar, CalTech | [CMS-165](http:\u002F\u002Ftensorlab.cms.caltech.edu\u002Fusers\u002Fanima\u002Fcms165-2020.html) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLVNifWxslHCDlbyitaLLYBOAEPbmF1AHg) | 2020 |\n| | | | | | |\n| 51. | **Machine Learning** | Rebecca Willett and Yuxin Chen, University of Chicago | [STAT 37710 \u002F CMSC 35400](https:\u002F\u002Fvoices.uchicago.edu\u002Fwillett\u002Fteaching\u002Fstats37710-cmsc35400-s20) | [Lecture-Videos](https:\u002F\u002Fvoices.uchicago.edu\u002Fwillett\u002Fteaching\u002Fstats37710-cmsc35400-s20) | S2020 |\n| 52. | **Introduction to Machine Learning** | Sanjay Lall and Stephen Boyd, Stanford University | [EE104\u002FCME107](http:\u002F\u002Fee104.stanford.edu) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLoROMvodv4rN_Uy7_wmS051_q1d6akXmK) | S2020 |\n| 53. | **Applied Machine Learning** | Andreas Müller, Columbia University | [COMS-W4995](https:\u002F\u002Fwww.cs.columbia.edu\u002F~amueller\u002Fcomsw4995s20\u002Fschedule\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL_pVmAaAnxIRnSw6wiCpSvshFyCREZmlM) | S2020 |\n| 54. | **Statistical Machine Learning** | Ulrike von Luxburg, Eberhard Karls Universität Tübingen | [Stat-ML](https:\u002F\u002Fwww.tml.cs.uni-tuebingen.de\u002Fteaching\u002F2020_statistical_learning\u002Findex.php) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL05umP7R6ij2XCvrRzLokX6EoHWaGA2cC) | SS2020 |\n| 55. | **Probabilistic Machine Learning** | Philipp Hennig, Eberhard Karls Universität Tübingen | [Prob-ML](https:\u002F\u002Funi-tuebingen.de\u002Fen\u002F180804) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL05umP7R6ij1tHaOFY96m5uX3J21a6yNd) | SS2020 |\n| 56. | **Machine Learning** | Sarath Chandar, PolyMTL, UdeM, Mila | [INF8953CE](http:\u002F\u002Fsarathchandar.in\u002Fteaching\u002Fml\u002Ffall2020) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLImtCgowF_ET0mi-AmmqQ0SIJUpWYaIOr) | F2020 |\n| 57. | **Machine Learning** | Erik Bekkers, Universiteit van Amsterdam | [UvA-ML](https:\u002F\u002Fuvaml1.github.io\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL8FnQMH2k7jzhtVYbKmvrMyXDYMmgjj_n) | F2020 |\n| 58. | **Neural Networks for Signal Processing** | Shayan Srinivasa Garani, Indian Institute of Science | [NN4SP](https:\u002F\u002Flabs.dese.iisc.ac.in\u002Fpnsil\u002Fneural-networks-and-learning-systems-i-fall-2020\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLgMDNELGJ1CZn1399dV7_U4VBNJflRsua) | F2020 |\n| 59. | **Introduction to Machine Learning** | Dmitry Kobak, Universität Klinikum Tübingen | `None` | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL05umP7R6ij35ShKLDqccJSDntugY4FQT) | 2020 |\n| 60. | **Machine Learning (PRML)** | Erik J. Bekkers, Universiteit van Amsterdam | [UvAML-1](https:\u002F\u002Fuvaml1.github.io) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL8FnQMH2k7jzhtVYbKmvrMyXDYMmgjj_n) | 2020 |\n| | | | | | |\n| 61. | **Machine Learning with Kernel Methods** | Julien Mairal and Jean-Philippe Vert, Inria\u002FENS Paris-Saclay, Google | [ML-Kernels](http:\u002F\u002Fmembers.cbio.mines-paristech.fr\u002F~jvert\u002Fsvn\u002Fkernelcourse\u002Fcourse\u002F2021mva\u002Findex.html) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLD93kGj6_EdrkNj27AZMecbRlQ1SMkp_o) | S2021 |\n| 62. | **Continual Learning** | Vincenzo Lomonaco, Università di Pisa | [ContLearn'21](https:\u002F\u002Fcourse.continualai.org\u002Fbackground\u002Fdetails) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLm6QXeaB-XkBfM5RgQP6wCR7Jegdg51Px) | 2021 |\n| 63. | **Causality** | Christina Heinze-Deml, ETH Zurich | [Causal'21](https:\u002F\u002Fstat.ethz.ch\u002Flectures\u002Fss21\u002Fcausality.php#course_materials) | [YouTube-Lectures](https:\u002F\u002Fstat.ethz.ch\u002Flectures\u002Fss21\u002Fcausality.php#course_materials) | 2021 |\n| | | | | | |\n\n[Go to Contents :arrow_heading_up:](https:\u002F\u002Fgithub.com\u002Fkmario23\u002Fdeep-learning-drizzle#contents) \n\n:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:\n### :balloon: Reinforcement Learning :hotsprings: :video_game: \n:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:\n\n| S.No | Course Name | University\u002FInstructor(s) | Course Webpage | Video Lectures | Year |\n| ---- | -------------------------------------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | ------ |\n| 1. | **A Short Course on Reinforcement Learning** | Satinder Singh, UMichigan | `None` | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLM4Pv4KYYzGy4cIFQ5C36-1jMNLab80Ky) | 2011 |\n| 2. | **Approximate Dynamic Programming** | Dimitri P. Bertsekas, MIT | [Lecture-Slides](http:\u002F\u002Fadpthu2014.weebly.com\u002Fslides--materials.html) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLiCLbsFQNFAxOmVeqPhI5er1LGf2-L9I4) | 2014 |\n| 3. | **Introduction to Reinforcement Learning** | David Silver, DeepMind | [UCL-RL](http:\u002F\u002Fwww0.cs.ucl.ac.uk\u002Fstaff\u002Fd.silver\u002Fweb\u002FTeaching.html) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLqYmG7hTraZDM-OYHWgPebj2MfCFzFObQ) | 2015 |\n| 4. | **Reinforcement Learning** | Charles Isbell, Chris Pryby, GaTech; Michael Littman, Brown | [RL-Udacity](https:\u002F\u002Feu.udacity.com\u002Fcourse\u002Freinforcement-learning--ud600) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLAwxTw4SYaPnidDwo9e2c7ixIsu_pdSNp) | 2015 |\n| 5. | **Reinforcement Learning** | Balaraman Ravindran, IIT Madras | [RL-IITM](https:\u002F\u002Fwww.cse.iitm.ac.in\u002F~ravi\u002Fcourses\u002FReinforcement%20Learning.html) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLNdWVHi37UggQIVcaZcmtGGEQHY9W7d9D) | 2016 |\n| 6. | **Deep Reinforcement Learning** | Sergey Levine, UC Berkeley | [CS-294](http:\u002F\u002Frail.eecs.berkeley.edu\u002Fdeeprlcoursesp17\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLkFD6_40KJIwTmSbCv9OVJB3YaO4sFwkX) | S2017 |\n| 7. | **Deep Reinforcement Learning** | Sergey Levine, UC Berkeley | [CS-294](http:\u002F\u002Frail.eecs.berkeley.edu\u002Fdeeprlcourse-fa17\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLkFD6_40KJIznC9CDbVTjAF2oyt8_VAe3) | F2017 |\n| 8. | **Deep RL Bootcamp** | Many legends, UC Berkeley | [Deep-RL](https:\u002F\u002Fsites.google.com\u002Fview\u002Fdeep-rl-bootcamp\u002Flectures) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCTgM-VlXKuylPrZ_YGAJHOw\u002Fvideos) | 2017 |\n| 9 | **Data Efficient Reinforcement Learning** | Lots of Legends, Canary Islands | [DERL-17](http:\u002F\u002Fdalimeeting.org\u002Fdali2017\u002Fdata-efficient-reinforcement-learning.html) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL-tWvTpyd1VAvDpxukup6w-SuZQQ7e8K8) | 2017 |\n| 10. | **Deep Reinforcement Learning** | Sergey Levine, UC Berkeley | [CS-294-112](http:\u002F\u002Frail.eecs.berkeley.edu\u002Fdeeprlcourse-fa18\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLkFD6_40KJIxJMR-j5A1mkxK26gh_qg37) | 2018 |\n| | | | | | |\n| 11. | **Reinforcement Learning** | Pascal Poupart, University of Waterloo | [CS-885](https:\u002F\u002Fcs.uwaterloo.ca\u002F~ppoupart\u002Fteaching\u002Fcs885-spring18\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLdAoL1zKcqTXFJniO3Tqqn6xMBBL07EDc) | 2018 |\n| 12. | **Deep Reinforcement Learning and Control** | Katerina Fragkiadaki and Tom Mitchell, CMU | [10-703](http:\u002F\u002Fwww.andrew.cmu.edu\u002Fcourse\u002F10-703\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLpIxOj-HnDsNfvOwRKLsUobmnF2J1l5oV) | 2018 |\n| 13. | **Reinforcement Learning and Optimal Control** | Dimitri Bertsekas, Arizona State University | [RLOC](http:\u002F\u002Fweb.mit.edu\u002Fdimitrib\u002Fwww\u002FRLbook.html) | [Lecture-Videos](http:\u002F\u002Fweb.mit.edu\u002Fdimitrib\u002Fwww\u002FRLbook.html) | 2019 |\n| 14. | **Reinforcement Learning** | Emma Brunskill, Stanford University | [CS 234](http:\u002F\u002Fweb.stanford.edu\u002Fclass\u002Fcs234\u002Findex.html) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLoROMvodv4rOSOPzutgyCTapiGlY2Nd8u) | 2019 |\n| 15. | **Reinforcement Learning Day** | Lots of Legends, Microsoft Research, New York | [RLD-19](https:\u002F\u002Fwww.microsoft.com\u002Fen-us\u002Fresearch\u002Fevent\u002Freinforcement-learning-day-2019\u002F#!agenda) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLD7HFcN7LXRe9nWEX3Up-RiCDi6-0mqVC) | 2019 |\n| 16. | **New Directions in Reinforcement Learning and Control** | Lots of Legends, IAS, Princeton University | [NDRLC-19](https:\u002F\u002Fwww.math.ias.edu\u002Fndrlc) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLdDZb3TwJPZ61sGqd6cbWCmTc275NrKu3) | 2019 |\n| 17. | **Deep Reinforcement Learning** | Sergey Levine, UC Berkeley | [CS 285](http:\u002F\u002Frail.eecs.berkeley.edu\u002Fdeeprlcourse-fa19) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLkFD6_40KJIwhWJpGazJ9VSj9CFMkb79A) | F2019 |\n| 18. | **Deep Multi-Task and Meta Learning** | Chelsea Finn, Stanford University | [CS 330](https:\u002F\u002Fcs330.stanford.edu\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLoROMvodv4rMC6zfYmnD7UG3LVvwaITY5) | F2019 |\n| 19. | **RL-Theory Seminars** | Lots of Legends, Earth | [RL-theory-sem](https:\u002F\u002Fsites.google.com\u002Fview\u002Frltheoryseminars\u002Fpast-seminars) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCfBFutC9RbKK6p--B4R9ebA\u002Fvideos) | 2020 - |\n| 20. | **Deep Reinforcement Learning** | Sergey Levine, UC Berkeley | [CS 285](http:\u002F\u002Frail.eecs.berkeley.edu\u002Fdeeprlcourse-fa20) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL_iWQOsE6TfURIIhCrlt-wj9ByIVpbfGc) | F2020 |\n| | | | | | |\n| 21. | **Introduction to Reinforcement Learning** | Amir-massoud Farahmand, Vector Institute, University of Toronto | [RL-intro](https:\u002F\u002Famfarahmand.github.io\u002FIntroRL) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLCveiXxL2xNbiDq51a8iJwPRq2aO0ykrq) | S2021 |\n| 22. | **Reinforcement Learning** | Antonio Celani and Emanuele Panizon, International Centre for Theoretical Physics | `None` | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLp0hSY2uBeP8q2G3mfHGVGvQFEMX0QRWM) | 2021 |\n| 23. | **Computational Sensorimotor Learning** | Pulkit Agrawal, MIT-CSAIL | [6.884-CSL](https:\u002F\u002Fpulkitag.github.io\u002F6.884\u002Flectures) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLwNwxAG-kBxPMTIs2fKWSsf7HqL2TcC78) | S2021 |\n| 24. | **Reinforcement Learning** | Dimitri P. Bertsekas, ASU\u002FMIT | [RL-21](http:\u002F\u002Fweb.mit.edu\u002Fdimitrib\u002Fwww\u002FRLbook.html) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLmH30BG15SIp79JRJ-MVF12uvB1qPtPzn) | S2021 |\n| 25. | **Reinforcement Learning** | Sarath Chandar, École Polytechnique de Montréal | [INF8953DE](https:\u002F\u002Fchandar-lab.github.io\u002FINF8953DE) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLImtCgowF_ES_JdF_UcM60EXTcGZg67Ua) | F2021 |\n| 26. | **Deep Reinforcement Learning** | Sergey Levine, UC Berkeley | [CS 285](http:\u002F\u002Frail.eecs.berkeley.edu\u002Fdeeprlcourse) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL_iWQOsE6TfXxKgI1GgyV1B_Xa0DxE5eH) | F2021 |\n| 27. | **Reinforcement Learning Lecture Series** | Lots of Legends, DeepMind & UC London | [RL-series](https:\u002F\u002Fdeepmind.com\u002Flearning-resources\u002Freinforcement-learning-series-2021) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLqYmG7hTraZDVH599EItlEWsUOsJbAodm) | 2021 |\n| 28. | **Reinforcement Learning** | Dimitri P. Bertsekas, ASU\u002FMIT | [RL-22](http:\u002F\u002Fweb.mit.edu\u002Fdimitrib\u002Fwww\u002FRLbook.html) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLmH30BG15SIoXhxLldoio0BhsIY84YMDj) | S2022 |\n| | | | | | |\n\n[Go to Contents :arrow_heading_up:](https:\u002F\u002Fgithub.com\u002Fkmario23\u002Fdeep-learning-drizzle#contents) \n\n:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:\n### :loudspeaker: Probabilistic Graphical Models :sparkles: \n:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:\n\n| S.No | Course Name | University\u002FInstructor(s) | Course WebPage | Lecture Videos | Year |\n| ---- | ------------------------------------------------------------ | --------------------------------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------- |\n| 1. | **Probabilistic Graphical Models** | Many Legends, MPI-IS | [MLSS-Tuebingen](http:\u002F\u002Fmlss.tuebingen.mpg.de\u002F2013\u002F2013\u002Fspeakers.html) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLL0GjJzXhAWTRiW_ynFswMaiLSa0hjCZ3) | 2013 |\n| 2. | **Probabilistic Modeling and Machine Learning** | Zoubin Ghahramani, University of Cambridge | [WUST-Wroclaw](https:\u002F\u002Fwww.ii.pwr.edu.pl\u002F~gonczarek\u002Fzoubin.html) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLwUOK5j_XOsdfVAGKErx9HqnrVZIuRbZ2) | 2013 |\n| 3. | **Probabilistic Graphical Models** | Eric Xing, CMU | [10-708](http:\u002F\u002Fwww.cs.cmu.edu\u002F~epxing\u002FClass\u002F10708\u002Flecture.html) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLI3nIOD-p5aoXrOzTd1P6CcLavu9rNtC-) | 2014 |\n| 4. | **Learning with Structured Data: An Introduction to Probabilistic Graphical Models** | Christoph Lampert, IST Austria | `None` | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLEqoHzpnmTfA0wc1JxjoVVOrJlx8W0rGf) | 2016 |\n| 5. | **Probabilistic Graphical Models** | Nicholas Zabaras, University of Notre Dame | [PGM](https:\u002F\u002Fwww.zabaras.com\u002Fprobabilistic-graphical-models) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLd-PuDzW85AcV4bgdu7wHPL37hm60W4RM) | 2018 |\n| 6. | **Probabilistic Graphical Models** | Eric Xing, CMU | [10-708](https:\u002F\u002Fsailinglab.github.io\u002Fpgm-spring-2019\u002F) | [Lecture-Videos](https:\u002F\u002Fsailinglab.github.io\u002Fpgm-spring-2019\u002Flectures) \u003Cbr> [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLoZgVqqHOumTY2CAQHL45tQp6kmDnDcqn) | S2019 |\n| 7. | **Probabilistic Graphical Models** | Eric Xing, CMU | [10-708](https:\u002F\u002Fwww.cs.cmu.edu\u002F~epxing\u002FClass\u002F10708-20\u002Findex.html) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLoZgVqqHOumTqxIhcdcpOAJOOimrRCGZn) | S2020 |\n| 8. | **Uncertainty Modeling in AI** | Gim Hee Lee, National University of Singapura (NUS) | [CS 5340 - CH](https:\u002F\u002Fwww.coursehero.com\u002Fsitemap\u002Fschools\u002F2652-National-University-of-Singapore\u002Fcourses\u002F7821096-CS5340\u002F), [CS 5340-NB](https:\u002F\u002Fgithub.com\u002Fclear-nus\u002FCS5340-notebooks) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLxg0CGqViygOb9Eyc8IXM27doxjp2SK0H) | 2020-21 |\n| | | | | | |\n\n[Go to Contents :arrow_heading_up:](https:\u002F\u002Fgithub.com\u002Fkmario23\u002Fdeep-learning-drizzle#contents)\n\n:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:\n## :game_die: Bayesian Deep Learning :spades: :gem: \n:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:\n\n| S.No | Course Name | University\u002FInstructor(s) | Course WebPage | Lecture Videos | Year |\n| ---- | --------------------------------------------------- | --------------------------------- | -------------------------------------------------------- | ------------------------------------------------------------ | -------- |\n| 1. | **Bayesian Neural Networks, Variational Inference** | Lots of Legends | `None` | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLM4Pv4KYYzGwUB4bFy183hwGhpL9ytvA1) | 2014-now |\n| 2. | **Variational Inference** | Chieh Wu, Northeastern University | `None` | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLdk2fd27CQzSd1sQ3kBYL4vtv6GjXvPsE) | 2015 |\n| 3. | **Deep Learning and Bayesian Methods** | Lots of Legends, HSE Moscow | [DLBM-SS](http:\u002F\u002Fdeepbayes.ru\u002F2018) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLe5rNUydzV9Q01vWCP9BV7NhJG3j7mz62) | 2018 |\n| 4. | **Deep Learning and Bayesian Methods** | Lots of Legends, HSE Moscow | [DLBM-SS](http:\u002F\u002Fdeepbayes.ru\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLe5rNUydzV9QHe8VDStpU0o8Yp63OecdW) | 2019 |\n| 5. | **Nordic Probabilistic AI** | Lots of Legends, NTNU, Trondheim | [ProbAI](https:\u002F\u002Fgithub.com\u002Fprobabilisticai\u002Fprobai-2019) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLRy-VW__9hV8s--JkHXZvnd26KgjRP2ik) | 2019 |\n| | | | | | |\n\n[Go to Contents :arrow_heading_up:](https:\u002F\u002Fgithub.com\u002Fkmario23\u002Fdeep-learning-drizzle#contents)\n\n:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:\n## :movie_camera: Medical Imaging :camera: :video_camera: \n:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:\n\n| S.No | Course Name | University\u002FInstructor(s) | Course WebPage | Lecture Videos | Year |\n| ---- | ------------------------------------------------------------ | ------------------------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ----- |\n| 1. | **Medical Imaging Summer School** | Lots of Legends, Sicily | [MISS-14](http:\u002F\u002Fiplab.dmi.unict.it\u002Fmiss14\u002Fprogramme.html) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL_VeUGLULXQtvcCdAgmvKoJ1k0Ajhz-Qu) | 2014 |\n| 2. | **Biomedical Image Analysis Summer School** | Lots of Legends, Paris | `None` | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLgSHH6boFf5uJAUT4ZRiAZc_ofXolkAGK) | 2015 |\n| 3. | **Medical Imaging Summer School** | Lots of Legends, Sicily | [MISS-16](http:\u002F\u002Fiplab.dmi.unict.it\u002Fmiss16\u002Fprogramme.html) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLTRCr47yTx5iXIYSneX3LKf16upaw59wa) | 2016 |\n| 4. | **OPtical and UltraSound imaging - OPUS** | Lots of Legends, Université de Lyon, France | [OPUS'16](https:\u002F\u002Fopus2016lyon.sciencesconf.org\u002Fresource\u002Fpage\u002Fid\u002F2) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL95ayoVLX8GdUKbxu-R9WqRWwzdWcKjti) | 2016 |\n| 5. | **Medical Imaging Summer School** | Lots of Legends, Sicily | [MISS-18](http:\u002F\u002Fiplab.dmi.unict.it\u002Fmiss\u002Fprogramme.htm) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL_VeUGLULXQux1dV4iA3XuMX6AueJmGGa) | 2018 |\n| 6. | **Seminar on AI in Healthcare** | Lots of Legends, Stanford | [CS 522](http:\u002F\u002Fcs522.stanford.edu\u002F2018\u002Findex.html) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLYn-ZmPR1DtNQJ-ot-L2V2EgUEH6OH_7w) | 2018 |\n| 7. | **Machine Learning for Healthcare** | David Sontag, Peter Szolovits, CSAIL MIT | [MLHC-19](https:\u002F\u002Fmlhc19mit.github.io\u002F) \u003Cbr\u002F>[MIT 6.S897](https:\u002F\u002Focw.mit.edu\u002Fcourses\u002Felectrical-engineering-and-computer-science\u002F6-s897-machine-learning-for-healthcare-spring-2019\u002Flecture-notes\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLUl4u3cNGP60B0PQXVQyGNdCyCTDU1Q5j) | S2019 |\n| 8. | **Deep Learning and Medical Applications** | Lots of Legends, IPAM, UCLA | [DLM-20](https:\u002F\u002Fwww.ipam.ucla.edu\u002Fprograms\u002Fworkshops\u002Fdeep-learning-and-medical-applications\u002F?tab=schedule) | [Lecture-Videos](https:\u002F\u002Fwww.ipam.ucla.edu\u002Fprograms\u002Fworkshops\u002Fdeep-learning-and-medical-applications\u002F?tab=schedule) | 2020 |\n| 9. | **Stanford Symposium on Artificial Intelligence in Medicine and Imaging** | Lots of Legends, Stanford AIMI | [AIMI-20](https:\u002F\u002Faimi.stanford.edu\u002Fnews-events\u002Faimi-symposium\u002Fagenda) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=tR2ObiL4il8&list=PLe6zdIMe5B7IR0oDOobXBDBlYY1eqLYPx) | 2020 |\n| | | | | | |\n\n[Go to Contents :arrow_heading_up:](https:\u002F\u002Fgithub.com\u002Fkmario23\u002Fdeep-learning-drizzle#contents)\n\n:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:\n## :tada: Graph Neural Networks (Geometric DL) :confetti_ball: :balloon: \n:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:\n\n| S.No | Course Name | University\u002FInstructor(s) | Course WebPage | Lecture Videos | Year |\n| ---- | ------------------------------------------------------------ | ------------------------------------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ----- |\n| 1. | **Deep learning on graphs and manifolds** | Michael Bronstein, Technion | `None` | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLH39kM3nuavcVOUIIBraBNHjv-CwEd1uV) | 2017 |\n| 2. | **Geometric Deep Learning on Graphs and Manifolds** | Michael Bronstein, Technische Universität München | `None` | [Lec-part1](https:\u002F\u002Fstreams.tum.de\u002FMediasite\u002FPlay\u002F1f3b894e78f6400daa7885c886b936fb1d), \u003Cbr\u002F>[Lec-part2](https:\u002F\u002Fstreams.tum.de\u002FMediasite\u002FPlay\u002F6039c846b2f84e7a806024c06e3f5c5c1d) | 2017 |\n| 3. | **Eurographics Symposium on Geometry Processing - Graduate School** | Lots of Legends, SIGGRAPH, London | [SGP-2017](http:\u002F\u002Fgeometry.cs.ucl.ac.uk\u002FSGP2017\u002F?p=gradschool) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLOp-ngXvomHArqntgLVNzuJNdzNx3rDjZ) | 2017 |\n| 4. | **Eurographics Symposium on Geometry Processing - Graduate School** | Lots of Legends, SIGGRAPH, Paris | [SGP-2018](https:\u002F\u002Fsgp2018.sciencesconf.org\u002Fresource\u002Fpage\u002Fid\u002F7) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLvcoRb-DvAmgpp8LYw7dUvLxh-1Vrrm-v) | 2018 |\n| 5. | **Analysis of Networks: Mining and Learning with Graphs** | Jure Leskovec, Stanford University | [CS224W](http:\u002F\u002Fsnap.stanford.edu\u002Fclass\u002Fcs224w-2018\u002F) | [Lecture-Videos](http:\u002F\u002Fsnap.stanford.edu\u002Fclass\u002Fcs224w-2018\u002F) | 2018 |\n| 6. | **Machine Learning with Graphs** | Jure Leskovec, Stanford University | [CS224W](http:\u002F\u002Fsnap.stanford.edu\u002Fclass\u002Fcs224w-2019\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL-Y8zK4dwCrQyASidb2mjj_itW2-YYx6-) | 2019 |\n| 7. | Geometry and Learning from Data in 3D and Beyond -**Geometry and Learning from Data Tutorials** | Lots of Legends, IPAM UCLA | [GLDT](http:\u002F\u002Fwww.ipam.ucla.edu\u002Fprograms\u002Fworkshops\u002Fgeometry-and-learning-from-data-tutorials) | [Lecture-Videos](http:\u002F\u002Fwww.ipam.ucla.edu\u002Fprograms\u002Fworkshops\u002Fgeometry-and-learning-from-data-tutorials\u002F?tab=schedule) | 2019 |\n| 8. | Geometry and Learning from Data in 3D and Beyond - **Geometric Processing** | Lots of Legends, IPAM UCLA | [GeoPro](http:\u002F\u002Fwww.ipam.ucla.edu\u002Fprograms\u002Fworkshops\u002Fworkshop-i-geometric-processing\u002F) | [Lecture-Videos](http:\u002F\u002Fwww.ipam.ucla.edu\u002Fprograms\u002Fworkshops\u002Fworkshop-i-geometric-processing\u002F?tab=schedule) | 2019 |\n| 9. | Geometry and Learning from Data in 3D and Beyond - **Shape Analysis** | Lots of Legends, IPAM UCLA | [Shape-Analysis](http:\u002F\u002Fwww.ipam.ucla.edu\u002Fprograms\u002Fworkshops\u002Fworkshop-ii-shape-analysis\u002F) | [Lecture-Videos](http:\u002F\u002Fwww.ipam.ucla.edu\u002Fprograms\u002Fworkshops\u002Fworkshop-ii-shape-analysis\u002F?tab=schedule) | 2019 |\n| 10. | Geometry and Learning from Data in 3D and Beyond - **Geometry of Big Data** | Lots of Legends, IPAM UCLA | [Geo-BData](http:\u002F\u002Fwww.ipam.ucla.edu\u002Fprograms\u002Fworkshops\u002Fworkshop-iii-geometry-of-big-data) | [Lecture-Videos](http:\u002F\u002Fwww.ipam.ucla.edu\u002Fprograms\u002Fworkshops\u002Fworkshop-iii-geometry-of-big-data\u002F?tab=schedule) | 2019 |\n| | | | | | |\n| 11. | Geometry and Learning from Data in 3D and Beyond - **Deep Geometric Learning of Big Data and Applications** | Lots of Legends, IPAM UCLA | [DGL-BData](http:\u002F\u002Fwww.ipam.ucla.edu\u002Fprograms\u002Fworkshops\u002Fworkshop-iv-deep-geometric-learning-of-big-data-and-applications) | [Lecture-Videos](http:\u002F\u002Fwww.ipam.ucla.edu\u002Fprograms\u002Fworkshops\u002Fworkshop-iv-deep-geometric-learning-of-big-data-and-applications\u002F?tab=schedule) | 2019 |\n| 12. | **Israeli Geometric Deep Learning** | Lots of Legends, Israel | [iGDL-20](https:\u002F\u002Fgdl-israel.github.io\u002Fschedule.html) | [Lecture-Videos](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=c8_32IVn-sg) | 2020 |\n| 13. | **Machine Learning for Graphs and Sequential Data** | Stephan Günnemann, Technische Universität München (TUM) | [MLGS-20](https:\u002F\u002Fwww.in.tum.de\u002Fen\u002Fdaml\u002Fteaching\u002Fsummer-term-2020\u002Fmachine-learning-for-graphs-and-sequential-data\u002F) | [Lecture-Videos](https:\u002F\u002Fwww.in.tum.de\u002Fdaml\u002Fteaching\u002Fmlgs\u002F) | S2020 |\n| 14. | **Machine Learning with Graphs** | Jure Leskovec, Stanford | [CS224W](http:\u002F\u002Fweb.stanford.edu\u002Fclass\u002Fcs224w) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLoROMvodv4rPLKxIpqhjhPgdQy7imNkDn) | W2021 |\n| 15. | **Geometric Deep Learning** - AMMI | Lots of Legends, Virtual | [GDL-AMMI](https:\u002F\u002Fgeometricdeeplearning.com\u002Flectures) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLn2-dEmQeTfQ8YVuHBOvAhUlnIPYxkeu3) | 2021 |\n| 16. | **Summer School on Geometric Deep Learning** - | Lots of Legends, DTU, DIKU & AAU | [GDL- DTU, DIKU & AAU](https:\u002F\u002Fgeometric-deep-learning.compute.dtu.dk) | [Lecture-Videos](https:\u002F\u002Fgeometric-deep-learning.compute.dtu.dk\u002Ftalks-and-materials) | 2021 |\n| 17. | **Graph Neural Networks** | Alejandro Ribeiro, University of Pennsylvania | [ESE 514](https:\u002F\u002Fgnn.seas.upenn.edu) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUC_YPrqpiEqkeGOG1TCt0giQ\u002Fplaylists) | F2021 |\n| | | | | | |\n\n[Go to Contents :arrow_heading_up:](https:\u002F\u002Fgithub.com\u002Fkmario23\u002Fdeep-learning-drizzle#contents)\n\n:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:\n### :hibiscus: Natural Language Processing :cherry_blossom: :sparkling_heart: \n:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:\n\n| S.No | Course Name | University\u002FInstructor(s) | Course WebPage | Lecture Videos | Year |\n| ---- | --------------------------------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | --------- |\n| 1. | **Computational Linguistics I** | Jordan Boyd-Graber, University of Maryland | [CMS-723](http:\u002F\u002Fusers.umiacs.umd.edu\u002F~jbg\u002Fteaching\u002FCMSC_723\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLegWUnz91WfuPebLI97-WueAP90JO-15i) | 2013-2018 |\n| 2. | **Deep Learning for Natural Language Processing** | Nils Reimers, TU Darmstadt | [DL4NLP](https:\u002F\u002Fgithub.com\u002FUKPLab\u002Fdeeplearning4nlp-tutorial) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUC1zCuTrfpjT6Sv2kJk-JkvA\u002Fvideos) | 2015-2017 |\n| 3. | **Deep Learning for Natural Language Processing** | Many Legends, DeepMind-Oxford | [DL-NLP](http:\u002F\u002Fwww.cs.ox.ac.uk\u002Fteaching\u002Fcourses\u002F2016-2017\u002Fdl\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL613dYIGMXoZBtZhbyiBqb0QtgK6oJbpm) | 2017 |\n| 4. | **Deep Learning for Speech & Language** | UPC Barcelona | [DL-SL](https:\u002F\u002Ftelecombcn-dl.github.io\u002F2017-dlsl\u002F) | [Lecture-Videos](https:\u002F\u002Ftelecombcn-dl.github.io\u002F2017-dlsl\u002F) | 2017 |\n| 5. | **Neural Networks for Natural Language Processing** | Graham Neubig, CMU | [NN4NLP](http:\u002F\u002Fwww.phontron.com\u002Fclass\u002Fnn4nlp2017\u002F) [Code](https:\u002F\u002Fgithub.com\u002Fneubig\u002Fnn4nlp-code) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL8PYTP1V4I8ABXzdqtOpB_eqBlVAz_xPT) | 2017 |\n| 6. | **Neural Networks for Natural Language Processing** | Graham Neubig, CMU | [NN4-NLP](http:\u002F\u002Fwww.phontron.com\u002Fclass\u002Fnn4nlp2018\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL8PYTP1V4I8Ba7-rY4FoB4-jfuJ7VDKEE) | 2018 |\n| 7. | **Deep Learning for NLP** | Min-Yen Kan, NUS | [CS-6101](https:\u002F\u002Fwww.comp.nus.edu.sg\u002F~kanmy\u002Fcourses\u002F6101_1810\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLllwxvcS7ca5eD44KTCiT7Rmu_hFAafXB) | 2018 |\n| 8. | **Neural Networks for Natural Language Processing** | Graham Neubig, CMU | [NN4NLP](http:\u002F\u002Fwww.phontron.com\u002Fclass\u002Fnn4nlp2019\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL8PYTP1V4I8Ajj7sY6sdtmjgkt7eo2VMs) | 2019 |\n| 9. | **Natural Language Processing with Deep Learning** | Abigail See, Chris Manning, Richard Socher, Stanford University | [CS224n](http:\u002F\u002Fweb.stanford.edu\u002Fclass\u002Fcs224n\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLoROMvodv4rOhcuXMZkNm7j3fVwBBY42z) | 2019 |\n| 10. | **Natural Language Understanding** | Bill MacCartney and Christopher Potts | [CS224U](https:\u002F\u002Fweb.stanford.edu\u002Fclass\u002Fcs224u) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLoROMvodv4rObpMCir6rNNUlFAn56Js20) | S2019 |\n| | | | | | |\n| 11. | **Neural Networks for Natural Language Processing** | Graham Neubig, Carnegie Mellon University | [CS 11-747](http:\u002F\u002Fwww.phontron.com\u002Fclass\u002Fnn4nlp2020\u002Fschedule.html) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL8PYTP1V4I8CJ7nMxMC8aXv8WqKYwj-aJ) | S2020 |\n| 12. | **Advanced Natural Language Processing** | Mohit Iyyer, UMass Amherst | [CS 685](https:\u002F\u002Fpeople.cs.umass.edu\u002F~miyyer\u002Fcs685) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLWnsVgP6CzadmQX6qevbar3_vDBioWHJL) | F2020 |\n| 13. | **Machine Translation** | Philipp Koehn, Johns Hopkins University | [EN 601.468\u002F668](http:\u002F\u002Fmt-class.org\u002Fjhu\u002Fsyllabus.html) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLQrCiUDqDLG0lQX54o9jB4phJ-SLI6ZBQ) | F2020 |\n| 14. | **Neural Networks for NLP** | Graham Neubig, Carnegie Mellon University | [CS 11-747](http:\u002F\u002Fwww.phontron.com\u002Fclass\u002Fnn4nlp2021) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL8PYTP1V4I8AkaHEJ7lOOrlex-pcxS-XV) | 2021 |\n| 15. | **Deep Learning for Natural Language Processing** | Kyunghyun Cho, New York University | [DS-GA 1011](https:\u002F\u002Fdrive.google.com\u002Fdrive\u002Ffolders\u002F1ykXBtophaY_65VHK_8yDzZQJwfJDD5Ve) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLdH9u0f1XKW_s-c8EcgJpn_HJz5Jj1IRf) | F2021 |\n| 16. | **Natural Language Processing with Deep Learning** | Chris Manning, Stanford University | [CS224n](https:\u002F\u002Fweb.stanford.edu\u002Fclass\u002Farchive\u002Fcs\u002Fcs224n\u002Fcs224n.1214\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLoROMvodv4rOSH4v6133s9LFPRHjEmbmJ) | 2021 |\n| | | | | | |\n\n[Go to Contents :arrow_heading_up:](https:\u002F\u002Fgithub.com\u002Fkmario23\u002Fdeep-learning-drizzle#contents) \n\n:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:\n### :speaking_head: Automatic Speech Recognition :speech_balloon: :thought_balloon:\n:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:\n\n| S.No | Course Name | University\u002FInstructor(s) | Course WebPage | Lecture Videos | Year |\n| ---- | ---------------------------------------- | ------------------------------ | --------------------------------------------------- | ------------------------------------------------------------ | --------- |\n| 1. | **Deep Learning for Speech & Language** | UPC Barcelona | [DL-SL](https:\u002F\u002Ftelecombcn-dl.github.io\u002F2017-dlsl\u002F) | [Lecture-Videos](https:\u002F\u002Ftelecombcn-dl.github.io\u002F2017-dlsl\u002F) \u003Cbr\u002F> [YouTube-Videos](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL-5DCZHuHZkWeF9ljIjoC_X5gHRLNtIkU) | 2017 |\n| 2. | **Speech and Audio in the Northeast** | Many Legends, Google NYC | [SANE-15](http:\u002F\u002Fwww.saneworkshop.org\u002Fsane2015\u002F) | [YouTube-Videos](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLBJWRPcgwk7sZOB4UTVilWWnRg84L9o5i) | 2015 |\n| 3. | **Automatic Speech Recognition** | Samudra Vijaya K, TIFR | `None` | [YouTube-Videos](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCHk6uq1Cr9J3k5KNmIsYUNw\u002Fvideos) | 2016 |\n| 4. | **Speech and Audio in the Northeast** | Many Legends, Google NYC | [SANE-17](http:\u002F\u002Fwww.saneworkshop.org\u002Fsane2017\u002F) | [YouTube-Videos](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLBJWRPcgwk7tNLaBVu_S90ZQSblO3bwjg) | 2017 |\n| 5. | **Speech and Audio in the Northeast** | Many Legends, Google Cambridge | [SANE-18](http:\u002F\u002Fwww.saneworkshop.org\u002Fsane2018\u002F) | [YouTube-Videos](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLBJWRPcgwk7sjMANn8jqosyHIMe6DJhmn) | 2018 |\n| | | | | | |\n| -1. | **Deep Learning for Speech Recognition** | Many Legends, AoE | `None` | [YouTube-Videos](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLM4Pv4KYYzGyFYCXV6YPWAKVOR2gmHnQd) | 2015-2018 |\n\n[Go to Contents :arrow_heading_up:](https:\u002F\u002Fgithub.com\u002Fkmario23\u002Fdeep-learning-drizzle#contents) \n\n\n:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:\n### :fire: Modern Computer Vision :camera_flash: :movie_camera: \n:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:\n\n| S.No | Course Name | University\u002FInstructor(s) | Course WebPage | Lecture Videos | Year |\n| ---- | ------------------------------------------------------------ | ------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | ---------- |\n| 1. | **Microsoft Computer Vision Summer School** - (classical) | Lots of Legends, Lomonosov Moscow State University | `None` | [YouTube-Videos](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLbwKcm5vdiSYU54xFUG1zoxQTulqvIcJu) \u003Cbr> [Russian-mirror](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL-_cKNuVAYAUp0eCL7KO8QY4ETY3tIDFH) | 2011 |\n| 2. | **Computer Vision** - (classical) | Mubarak Shah, UCF | [CAP-5415](http:\u002F\u002Fcrcv.ucf.edu\u002Fcourses\u002FCAP5415\u002FFall2012\u002Findex.php) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLd3hlSJsX_Imk_BPmB_H3AQjFKZS9XgZm) | 2012 |\n| 3. | **Image and Multidimensional Signal Processing** - (classical) | William Hoff, Colorado School of Mines | [CSCI 510\u002FEENG 510](http:\u002F\u002Finside.mines.edu\u002F~whoff\u002Fcourses\u002FEENG510) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLyED3W677ALNv8Htn0f9Xh-AHe1aZPftv) | 2012 |\n| 4. | **Computer Vision** - (classical) | William Hoff, Colorado School of Mines | [CSCI 512\u002FEENG 512](http:\u002F\u002Finside.mines.edu\u002F~whoff\u002Fcourses\u002FEENG512\u002Findex.htm) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL4B3F8D4A5CAD8DA3) | 2012 |\n| 5. | **Image and Video Processing: From Mars to Hollywood with a Stop at the Hospital** | Guillermo Sapiro, Duke University | `None` | [YouTube-Videos](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLZ9qNFMHZ-A79y1StvUUqgyL-O0fZh2rs) | 2013 |\n| 6. | **Multiple View Geometry** (classical) | Daniel Cremers, Technische Universität München | [mvg](https:\u002F\u002Fvision.in.tum.de\u002Fteaching\u002Fss2014\u002Fmvg2014) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLTBdjV_4f-EJn6udZ34tht9EVIW7lbeo4) | 2013 |\n| 7. | **Mathematical Methods for Robotics, Vision, and Graphics** | Justin Solomon, Stanford University | [CS-205A](http:\u002F\u002Fgraphics.stanford.edu\u002Fcourses\u002Fcs205a\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLQ3UicqQtfNvQ_VzflHYKhAqZiTxOkSwi) | 2013 |\n| 8. | **Computer Vision** - (classical) | Mubarak Shah, UCF | [CAP-5415](http:\u002F\u002Fcrcv.ucf.edu\u002Fcourses\u002FCAP5415\u002FFall2014\u002Findex.php) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLd3hlSJsX_ImKP68wfKZJVIPTd8Ie5u-9) | 2014 |\n| 9. | **Computer Vision for Visual Effects** (classical) | Rich Radke, Rensselaer Polytechnic Institute | [ECSE-6969](https:\u002F\u002Fwww.ecse.rpi.edu\u002F~rjradke\u002Fcvfxcourse.html) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLuh62Q4Sv7BUJlKlt84HFqSWfW36MDd5a) | S2014 |\n| 10. | **Autonomous Navigation for Flying Robots** | Juergen Sturm, Technische Universität München | [Autonavx](https:\u002F\u002Fjsturm.de\u002Fwp\u002Fteaching\u002Fautonavx-slides\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLTBdjV_4f-EKBCUs1HmMtsnXv4JUoFrzg) | 2014 |\n| | | | | | |\n| 11. | **SLAM - Mobile Robotics** | Cyrill Stachniss, Universitaet Freiburg | [RobotMapping](http:\u002F\u002Fais.informatik.uni-freiburg.de\u002Fteaching\u002Fws13\u002Fmapping\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLgnQpQtFTOGQrZ4O5QzbIHgl3b1JHimN_) | 2014 |\n| 12. | **Computational Photography** | Irfan Essa, David Joyner, Arpan Chakraborty | [CP-Udacity](https:\u002F\u002Feu.udacity.com\u002Fcourse\u002Fcomputational-photography--ud955) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLAwxTw4SYaPn-unAWtRMleY4peSe4OzIY) | 2015 |\n| 13. | **Introduction to Digital Image Processing** | Rich Radke, Rensselaer Polytechnic Institute | [ECSE-4540](https:\u002F\u002Fwww.ecse.rpi.edu\u002F~rjradke\u002Fimproccourse.html) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLuh62Q4Sv7BUf60vkjePfcOQc8sHxmnDX) | S2015 |\n| 14. | **Lectures on Digital Photography** | Marc Levoy, Stanford\u002FGoogle Research | [LoDP](https:\u002F\u002Fsites.google.com\u002Fsite\u002Fmarclevoylectures\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL7ddpXYvFXspUN0N-gObF1GXoCA-DA-7i) | 2016 |\n| 15. | **Introduction to Computer Vision** (foundation) | Aaron Bobick, Irfan Essa, Arpan Chakraborty | [CV-Udacity](https:\u002F\u002Feu.udacity.com\u002Fcourse\u002Fintroduction-to-computer-vision--ud810) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLAwxTw4SYaPnbDacyrK_kB_RUkuxQBlCm) | 2016 |\n| 16. | **Computer Vision** | Syed Afaq Ali Shah, University of Western Australia | `None` | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLvqB6_mDBCdlnT84LK_NvbOqcXLlOTR8j) | 2016 |\n| 17. | **Photogrammetry I & II** | Cyrill Stachniss, University of Bonn | [PG-I&II](https:\u002F\u002Fwww.ipb.uni-bonn.de\u002Fphotogrammetry-i-ii\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLgnQpQtFTOGRsi5vzy9PiQpNWHjq-bKN1) | 2016 |\n| 18. | **Deep Learning for Computer Vision** | UPC Barcelona | [DLCV-16](http:\u002F\u002Fimatge-upc.github.io\u002Ftelecombcn-2016-dlcv\u002F) \u003Cbr\u002F> [DLCV-17](https:\u002F\u002Ftelecombcn-dl.github.io\u002F2017-dlcv\u002F) \u003Cbr\u002F> [DLCV-18](https:\u002F\u002Ftelecombcn-dl.github.io\u002F2018-dlcv\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL-5eMc3HQTBbuaTFP4wsfD2Y2VqEfQcaP) | 2016-2018 |\n| 19. | **Convolutional Neural Networks** | Andrew Ng, Stanford University | [DeepLearning.AI](https:\u002F\u002Fwww.deeplearning.ai\u002Fdeep-learning-specialization\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLkDaE6sCZn6Gl29AoE31iwdVwSG-KnDzF) | 2017 |\n| 20. | **Variational Methods for Computer Vision** | Daniel Cremers, Technische Universität München | [VMCV](https:\u002F\u002Fvision.in.tum.de\u002Fteaching\u002Fws2016\u002Fvmcv2016) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLTBdjV_4f-EJ7A2iIH5L5ztqqrWYjP2RI) | 2017 |\n| | | | | | |\n| 21. | **Winter School on Computer Vision** | Lots of Legends, Israel Institute for Advanced Studies | [WS-CV](http:\u002F\u002Fwww.as.huji.ac.il\u002Fcse) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLTn74Qx5mPsSniA5tt6W-o0OGYEeKScug) | 2017 |\n| 22. | **Deep Learning for Visual Computing** | Debdoot Sheet, IIT-Kgp | [Nptel](https:\u002F\u002Fonlinecourses.nptel.ac.in\u002Fnoc18_ee08\u002Fpreview) [Notebooks](https:\u002F\u002Fgithub.com\u002Fiitkliv\u002Fdlvcnptel) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLuv3GM6-gsE1Biyakccxb3FAn4wBLyfWf) | 2018 |\n| 23. | **The Ancient Secrets of Computer Vision** | Joseph Redmon, Ali Farhadi | [TASCV](https:\u002F\u002Fpjreddie.com\u002Fcourses\u002Fcomputer-vision\u002F) ; [TASCV-UW](https:\u002F\u002Fcourses.cs.washington.edu\u002Fcourses\u002Fcse455\u002F18sp\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLjMXczUzEYcHvw5YYSU92WrY8IwhTuq7p) | 2018 |\n| 24. | **Modern Robotics** | Kevin Lynch, Northwestern Robotics | [modern-robot](http:\u002F\u002Fhades.mech.northwestern.edu\u002Findex.php\u002FModern_Robotics) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLggLP4f-rq02vX0OQQ5vrCxbJrzamYDfx) | 2018 |\n| 25. | **Digial Image Processing** | Alex Bronstein, Technion | [CS236860](https:\u002F\u002Fvistalab-technion.github.io\u002Fcs236860\u002Finfo\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLM0a6Z788YAZOxUyWda9y3N_i2upIj1Ep) | 2018 |\n| 26. | **Mathematics of Imaging** - Variational Methods and Optimization in Imaging | Lots of Legends, Institut Henri Poincaré | [Workshop-1](http:\u002F\u002Fwww.ihp.fr\u002Fsites\u002Fdefault\u002Ffiles\u002Fconf1-04_au_08_fevr-imaging2019.pdf) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL9kd4mpdvWcAzD5Aq-P1TrLLiYckrloxw) | 2019 |\n| 27. | **Deep Learning for Video** | Xavier Giró, UPC Barcelona | [deepvideo](https:\u002F\u002Fmcv-m6-video.github.io\u002Fdeepvideo-2019\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL-5eMc3HQTBbPY-627Gornj09pZrNQgPD) | 2019 |\n| 28. | **Statistical modeling for shapes and imaging** | Lots of Legends, Institut Henri Poincaré, Paris | [workshop-2](https:\u002F\u002Fimaging-in-paris.github.io\u002Fsemester2019\u002Fworkshop2prog) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL9kd4mpdvWcAzD5Aq-P1TrLLiYckrloxw) | 2019 |\n| 29. | **Imaging and machine learning** | Lots of Legends, Institut Henri Poincaré, Paris | [workshop-3](https:\u002F\u002Fimaging-in-paris.github.io\u002Fsemester2019\u002Fworkshop3prog) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL9kd4mpdvWcAzD5Aq-P1TrLLiYckrloxw) | 2019 |\n| 30. | **Computer Vision** | Jayanta Mukhopadhyay, IIT Kgp | [CV-nptel](https:\u002F\u002Fnptel.ac.in\u002Fcourses\u002F106\u002F105\u002F106105216\u002F) | [YouTube-Lectures](https:\u002F\u002Fnptel.ac.in\u002Fcourses\u002F106\u002F105\u002F106105216\u002F) | 2019 |\n| | | | | | |\n| 31. | **Deep Learning for Computer Vision** | Justin Johnson, UMichigan | [EECS 498-007](https:\u002F\u002Fweb.eecs.umich.edu\u002F~justincj\u002Fteaching\u002Feecs498\u002F) | [Lecture-Videos](http:\u002F\u002Fleccap.engin.umich.edu\u002Fleccap\u002Fsite\u002Fjhygcph151x25gjj1f0) \u003Cbr\u002F> [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL5-TkQAfAZFbzxjBHtzdVCWE0Zbhomg7r) | 2019 |\n| 32. | **Sensors and State Estimation 2** | Cyrill Stachniss, University of Bonn | `None` | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLgnQpQtFTOGQh_J16IMwDlji18SWQ2PZ6) | S2020 |\n| 33. | **Computer Vision III: Detection, Segmentation and Tracking** | Laura Leal-Taixé, TU München | [CV3DST](https:\u002F\u002Fdvl.in.tum.de\u002Fteaching\u002Fcv3dst-ss20\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLog3nOPCjKBneGyffEktlXXMfv1OtKmCs) | S2020 |\n| 34. | **Advanced Deep Learning for Computer Vision** | Laura Leal-Taixé and Matthias Nießner, TU München | [ADL4CV](https:\u002F\u002Fdvl.in.tum.de\u002Fteaching\u002Fadl4cv-ss20) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLog3nOPCjKBnjhuHMIXu4ISE4Z4f2jm39) | S2020 |\n| 35. | **Computer Vision: Foundations** | Fred Hamprecht, Universität Heidelberg | [CVF](https:\u002F\u002Fhci.iwr.uni-heidelberg.de\u002Fial\u002Fcvf) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLuRaSnb3n4kRAbnmiyGd77hyoGzO9wPde) | SS2020 |\n| 36. | **MIT Vision Seminar** | Lots of Legends, MIT | [MIT-Vision](https:\u002F\u002Fsites.google.com\u002Fview\u002Fvisionseminar\u002Fpast-talks) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCLMiFkFyfcNnZs6iwYLPI9g\u002Fvideos) | 2015-now |\n| 37. | **TUM AI Guest Lectures** | Lots of Legends, Technische Universität München | [TUM-AI](https:\u002F\u002Fniessner.github.io\u002FTUM-AI-Lecture-Series) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLQ8Y4kIIbzy8kMlz7cRqz-BjbdyWsfLXt) | 2020 - now |\n| 38. | **Seminar on 3D Geometry & Vision** | Lots of Legends, Virtual | [3DGV seminar](https:\u002F\u002F3dgv.github.io) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLZk0jtN0g8e-xVTfsiV67q8Iz1cZO_FpV) | 2020 - now |\n| 39. | **Event-based Robot Vision** | Guillermo Gallego, Technische Universität Berlin | [EVIS-SS20](https:\u002F\u002Fsites.google.com\u002Fview\u002Fguillermogallego\u002Fteaching\u002Fevent-based-robot-vision) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL03Gm3nZjVgUFYUh3v5x8jVonjrGfcal8) | 2020 - now |\n| 40. | **Deep Learning for Computer Vision** | Vineeth Balasubramanian, IIT Hyderabad | [DL-CV'20](https:\u002F\u002Fonlinecourses.nptel.ac.in\u002Fnoc20_cs88\u002Fpreview) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLyqSpQzTE6M_PI-rIz4O1jEgffhJU9GgG) | 2020 |\n| | | | | | |\n| 41. | **Deep Learning for Visual Computing** | Peter Wonka, KAUST, SA | `NOne` | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLMpQLEui13s2DHbw6kTTxwQma8rehlfZE) | 2020 |\n| 42. | **Computer Vision** | Yogesh Rawat, University of Central Florida | [CAP5415-CV](https:\u002F\u002Fwww.crcv.ucf.edu\u002Fcourses\u002Fcap5415-fall-2020\u002Fschedule\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLd3hlSJsX_Ikm5il1HgmDB_z62BeoikFX) | F2020 |\n| 43. | **Multimedia Signal Processing** | Mark Hasegawa-Johnson, UIUC | [ECE-417 MSP](https:\u002F\u002Fcourses.engr.illinois.edu\u002Fece417\u002Ffa2020\u002F) | [Lecture Videos](https:\u002F\u002Fmediaspace.illinois.edu\u002Fchannel\u002FECE%20417\u002F26816181) | F2020 |\n| 44. | **Computer Vision** | Andreas Geiger, Universität Tübingen | [Comp.Vis](https:\u002F\u002Funi-tuebingen.de\u002Ffakultaeten\u002Fmathematisch-naturwissenschaftliche-fakultaet\u002Ffachbereiche\u002Finformatik\u002Flehrstuehle\u002Fautonomous-vision\u002Flectures\u002Fcomputer-vision\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL05umP7R6ij35L2MHGzis8AEHz7mg381_) | S2021 |\n| 45. | **3D Computer Vision** | Lee Gim Hee, National Univeristy of Singapura | `None` | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLxg0CGqViygP47ERvqHw_v7FVnUovJeaz) | 2021 |\n| 46. | **Deep Learning for Computer Vision: Fundamentals and Applications** | T. Dekel et al., Weizmann Institute of Science | [DL4CV](https:\u002F\u002Fdl4cv.github.io\u002Fschedule.html) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL_Z2_U9MIJdNgFM7-f2fZ9ZxjVRP_jhJv) | S2021 |\n| 47. | **Current Topics in ML Methods in 3D and Geometric Deep Learning** | Animesh Garg & others, University of Toronto | [CSC 2547](http:\u002F\u002Fwww.pair.toronto.edu\u002Fcsc2547-w21) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCrsmAXnwu6sgccWevW12Dfg\u002Fvideos) | 2021 |\n| 48. | **First Principles of Computer Vision** | Shree K. Nayar, Columbia University | [FPCV](https:\u002F\u002Ffpcv.cs.columbia.edu) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCf0WB91t8Ky6AuYcQV0CcLw\u002Fvideos) | 2021 |\n| 49. | **Self-Driving Cars** | Andreas Geiger, Universität Tübingen | [SDC'21](https:\u002F\u002Funi-tuebingen.de\u002Fde\u002F123611) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL05umP7R6ij321zzKXK6XCQXAaaYjQbzr) | W2021 |\n| | | | | | |\n\n[Go to Contents :arrow_heading_up:](https:\u002F\u002Fgithub.com\u002Fkmario23\u002Fdeep-learning-drizzle#contents) \n\n:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:\n### :star2: Boot Camps or Summer Schools :maple_leaf:\n:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:\n\n| S.No | Course Name | University\u002FInstructor(s) | Course WebPage | Lecture Videos | Year |\n| ---- | ------------------------------------------------------- | -------------------------------------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | --------- |\n| 1. | **Deep Learning, Feature Learning** | Lots of Legends, IPAM UCLA | [GSS-2012](https:\u002F\u002Fwww.ipam.ucla.edu\u002Fprograms\u002Fsummer-schools\u002Fgraduate-summer-school-deep-learning-feature-learning\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLHyI3Fbmv0SdzMHAy0aN59oYnLy5vyyTA) | 2012 |\n| 2. | **Big Data Boot Camp** | Lots of Legends, Simons Institute | [Big Data](https:\u002F\u002Fsimons.berkeley.edu\u002Fworkshops\u002Fschedule\u002F316) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLgKuh-lKre13RmUC2AybRvVAxO5DEMIBH) | 2013 |\n| 3. | **Machine Learning Summer School** | Lots of Legends, MPI-IS Tübingen | [MLSS-13](http:\u002F\u002Fmlss.tuebingen.mpg.de\u002F2013\u002F2013\u002Findex.html) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLqJm7Rc5-EXFv6RXaPZzzlzo93Hl0v91E) | 2013 |\n| 4 | **Graduate Summer School: Computer Vision** | Lots of Legends, IPAM-UCLA | [GSS-CV](http:\u002F\u002Fwww.ipam.ucla.edu\u002Fprograms\u002Fsummer-schools\u002Fgraduate-summer-school-computer-vision\u002F) | [Video-Lectures](http:\u002F\u002Fwww.ipam.ucla.edu\u002Fprograms\u002Fsummer-schools\u002Fgraduate-summer-school-computer-vision\u002F?tab=schedule) | 2013 |\n| 5. | **Machine Learning Summer School** | Lots of Legends, Reykjavik University | [MLSS-14](http:\u002F\u002Fmlss2014.hiit.fi\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLqdbxUnkqOw2nKn7VxYqIrKWcqRkQYOsF) | 2014 |\n| 6. | **Machine Learning Summer School** | Lots of Legends, Pittsburgh | [MLSS-14](http:\u002F\u002Fwww.mlss2014.com) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLZSO_6-bSqHQCIYxE3ycGLXHMjK3XV7Iz) | 2014 |\n| 7. | **Deep Learning Summer School** | Lots of Legends, Université de Montréal | [DLSS-15](https:\u002F\u002Fsites.google.com\u002Fsite\u002Fdeeplearningsummerschool\u002Fhome) | [YouTube-Lectures](http:\u002F\u002Fvideolectures.net\u002Fdeeplearning2015_montreal\u002F) | 2015 |\n| 8. | **Biomedical Image Analysis Summer School** | Lots of Legends, CentraleSupelec, Paris | `None` | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLgSHH6boFf5uJAUT4ZRiAZc_ofXolkAGK) | 2015 |\n| 9. | **Mathematics of Signal Processing** | Lots of Legends, Hausdorff Institute for Mathematics | [SigProc](http:\u002F\u002Fwww.him.uni-bonn.de\u002Fsignal-processing-2016\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLul8LCT3AJqSQo3lr5RbwxJ92RsgRuDtx) | 2016 |\n| 10. | **Microsoft Research - Machine Learning Course** | S V N Vishwanathan and Prateek Jain MS-Research | `None` | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL34iyE0uXtxo7vPXGFkmm6KbgZQwjf9Kf) | 2016 |\n| | | | | | |\n| 11. | **Deep Learning Summer School** | Lots of Legends, Université de Montréal | [DL-SS-16](https:\u002F\u002Fsites.google.com\u002Fsite\u002Fdeeplearningsummerschool2016\u002Fhome) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL5bqIc6XopCbb-FvnHmD1neVlQKwGzQyR) | 2016 |\n| 12. | **Lisbon Machine Learning School** | Lots of Legends, Instituto Superior Técnico, Portugal | [LxMLS-16](http:\u002F\u002Flxmls.it.pt\u002F2016\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLToLj8M4ao-fymxXBIOU6sF1NGFLb5EiX) | 2016 |\n| 13. | **Machine Learning Advances and Applications Seminar** | Lots of Legends, Fields Institute, University of Toronto | [MLAAS-16](http:\u002F\u002Fwww.fields.utoronto.ca\u002Factivities\u002F16-17\u002Fmachine-learning) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLfsVAYSMwskuQcRkuDApP40lX_i08d0QK) \u003Cbr\u002F> [Video-Lectures](http:\u002F\u002Fwww.fields.utoronto.ca\u002Fvideo-archive\u002Fevent\u002F2267) | 2016-2017 |\n| 14. | **Machine Learning Advances and Applications Seminar** | Lots of Legends, Fields Institute, University of Toronto | [MLAAS-17](http:\u002F\u002Fwww.fields.utoronto.ca\u002Factivities\u002F17-18\u002Fmachine-learning) | [Video Lectures](http:\u002F\u002Fwww.fields.utoronto.ca\u002Fvideo-archive\u002Fevent\u002F2487) | 2017-2018 |\n| 15. | **Machine Learning Summer School** | Lots of Legends, MPI-IS Tübingen | [MLSS-17](http:\u002F\u002Fmlss.tuebingen.mpg.de\u002F2017\u002Findex.html) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLqJm7Rc5-EXFUOvoYCdKikfck8YeUCnl9) | 2017 |\n| 16. | **Representation Learning** | Lots of Legends, Simons Institute | [RepLearn](https:\u002F\u002Fsimons.berkeley.edu\u002Fworkshops\u002Fabstracts\u002F3750) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLgKuh-lKre13UNV4ztsWUXciUZ7x_ZDHz) | 2017 |\n| 17. | **Foundations of Machine Learning** | Lots of Legends, Simons Institute | [ML-BootCamp](https:\u002F\u002Fsimons.berkeley.edu\u002Fworkshops\u002Fabstracts\u002F3748) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLgKuh-lKre11GbZWneln-VZDLHyejO7YD) | 2017 |\n| 18. | **Optimization, Statistics, and Uncertainty** | Lots of Legends, Simons Institute | [Optim-Stats](https:\u002F\u002Fsimons.berkeley.edu\u002Fworkshops\u002Fabstracts\u002F4795) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLgKuh-lKre13ACD44z2FH-IVP1e8ip5JO) | 2017 |\n| 19. | **Deep Learning: Theory, Algorithms, and Applications** | Lots of Legends, TU-Berlin | [DL: TAA](http:\u002F\u002Fdoc.ml.tu-berlin.de\u002Fdlworkshop2017\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLJOzdkh8T5kqCNV_v1w2tapvtJDZYiohW) | 2017 |\n| 20. | **Deep Learning and Reinforcement Learning Summer School** | Lots of Legends, Université de Montréal | [DLRL-2017](https:\u002F\u002Fmila.quebec\u002Fen\u002Fcours\u002Fdeep-learning-summer-school-2017\u002F) | [Lecture-videos](http:\u002F\u002Fvideolectures.net\u002Fdeeplearning2017_montreal\u002F) | 2017 |\n| | | | | | |\n| 21. | **Statistical Physics Methods in Machine Learning** | Lots of Legends, International Centre for Theoretical Sciences, TIFR | [SPMML](https:\u002F\u002Fwww.icts.res.in\u002Fdiscussion-meeting\u002FSPMML2017) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL04QVxpjcnjhtL3IIVyFRMOgdhWtPn7YJ) | 2017 |\n| 22. | **Lisbon Machine Learning School** | Lots of Legends, Instituto Superior Técnico, Portugal | [LxMLS-17](http:\u002F\u002Flxmls.it.pt\u002F2017\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLToLj8M4ao-fuRfnzEJCCnvuW2_FeJ73N) | 2017 |\n| 23. | **Interactive Learning** | Lots of Legends, Simons Institute, Berkeley | [IL-2017](https:\u002F\u002Fsimons.berkeley.edu\u002Fworkshops\u002Fschedule\u002F3749) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLgKuh-lKre10T2POF-WzXh0ckdpyvANUx) | 2017 |\n| 24. | **Computational Challenges in Machine Learning** | Lots of Legends, Simons Institute, Berkeley | [CCML-17](https:\u002F\u002Fsimons.berkeley.edu\u002Fworkshops\u002Fschedule\u002F3751) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLgKuh-lKre12eXz4dnvc8oervo2_Af4iU) | 2017 |\n| 25. | **Foundations of Data Science** | Lots of Legends, Simons Institute | [DS-BootCamp](https:\u002F\u002Fsimons.berkeley.edu\u002Fworkshops\u002Fabstracts\u002F6680) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLgKuh-lKre13r1Qrnrejj3f498-NurSf3) | 2018 |\n| 26. | **Deep Learning and Bayesian Methods** | Lots of Legends, HSE Moscow | [DLBM-SS](http:\u002F\u002Fdeepbayes.ru\u002F2018\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLe5rNUydzV9Q01vWCP9BV7NhJG3j7mz62) | 2018 |\n| 27. | **New Deep Learning Techniques** | Lots of Legends, IPAM UCLA | [IPAM-Workshop](https:\u002F\u002Fwww.ipam.ucla.edu\u002Fprograms\u002Fworkshops\u002Fnew-deep-learning-techniques\u002F?tab=schedule) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLHyI3Fbmv0SdM0zXj31HWjG9t9Q0v2xYN) | 2018 |\n| 28. | **Deep Learning and Reinforcement Learning Summer School** | Lots of Legends, University of Toronto | [DLRL-2018](https:\u002F\u002Fdlrlsummerschool.ca\u002F2018-event\u002F) | [Lecture-videos](http:\u002F\u002Fvideolectures.net\u002FDLRLsummerschool2018_toronto\u002F) | 2018 |\n| 29. | **Machine Learning Summer School** | Lots of Legends, Universidad Autónoma de Madrid, Spain | [MLSS-18](http:\u002F\u002Fmlss.ii.uam.es\u002Fmlss2018\u002Findex.html) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCbPJHr__eIor_7jFH3HmVHQ\u002Fvideos) \u003Cbr\u002F> [Course-videos](http:\u002F\u002Fmlss.ii.uam.es\u002Fmlss2018\u002Fspeakers.html) | 2018 |\n| 30. | **Theoretical Basis of Machine Learning** | Lots of Legends, International Centre for Theoretical Sciences, TIFR | [TBML-18](https:\u002F\u002Fwww.icts.res.in\u002Fdiscussion-meeting\u002Ftbml2018) | [Lecture-Videos](https:\u002F\u002Fwww.icts.res.in\u002Fdiscussion-meeting\u002Ftbml2018\u002Ftalks) \u003Cbr\u002F> [YouTube-Videos](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL04QVxpjcnjj1DgnXxFBo2fkSju4r-ggr) | 2018 |\n| | | | | | |\n| 31. | **Polish View on Machine Learning** | Lots of Legends, Warsaw | [PLinML-18](https:\u002F\u002Fplinml.mimuw.edu.pl\u002F) | [YouTube-Videos](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLoaWrlj9TDhPcA6N9dZQ6GPXboYuumDRp) | 2018 |\n| 32. | **Big Data Analysis in Astronomy** | Lots of Legends, Tenerife | [BDAA-18](http:\u002F\u002Fresearch.iac.es\u002Fwinterschool\u002F2018\u002Fpages\u002Fbook-ws2018.php) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLM4Pv4KYYzGx42W5pSp3Itetp0u-PENtI) | 2018 |\n| 33. | **Machine Learning Advances and Applications Seminar** | Lots of Legends, Fields Institute, University of Toronto | [MLASS](http:\u002F\u002Fwww.fields.utoronto.ca\u002Factivities\u002F18-19\u002Fmachine-learning) | [Video Lectures](http:\u002F\u002Fwww.fields.utoronto.ca\u002Fvideo-archive\u002Fevent\u002F2681) | 2018-2019 |\n| 34. | **MIFODS- ML, Stats, ToC seminar** | Lots of Legends, MIT | [MIFODS-seminar](http:\u002F\u002Fmifods.mit.edu\u002Fseminar.php) | [Lecture-videos](http:\u002F\u002Fmifods.mit.edu\u002Fseminar.php) | 2018-2019 |\n| 35. | **Learning Machines Seminar Series** | Lots of Legends, Cornell Tech | [LMSS](https:\u002F\u002Flmss.tech.cornell.edu\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLycW2Yy79JuxbQZ9uHEu_NS3cGNomhL2A) | 2018-now |\n| 36. | **Machine Learning Summer School** | Lots of Legends, South Africa | [MLSS'19](https:\u002F\u002Fmlssafrica.com\u002Fprogramme-schedule\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUC722CmQVgcLtxt_jXr3RyWg\u002Fvideos) | 2019 |\n| 37. | **Deep Learning Boot Camp** | Lots of Legends, Simons Institute, Berkeley | [DLBC-19](https:\u002F\u002Fsimons.berkeley.edu\u002Fworkshops\u002Fschedule\u002F10624) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLgKuh-lKre12c2Il9mNX0Cmp9Z4oFNrQh) | 2019 |\n| 38. | **Frontiers of Deep Learning** | Lots of Legends, Simons Institute, Berkeley | [FoDL-19](https:\u002F\u002Fsimons.berkeley.edu\u002Fworkshops\u002Fschedule\u002F10627) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLgKuh-lKre11ekU7g-Z_qsvjDD8cT-hi9) | 2019 |\n| 39. | **Mathematics of data: Structured representations for sensing, approximation and learning** | Lots of Legends, The Alan Turing Institute, London | [MoD-19](https:\u002F\u002Fwww.turing.ac.uk\u002Fsites\u002Fdefault\u002Ffiles\u002F2019-05\u002Fagenda_9_3.pdf) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLuD_SqLtxSdX_w1Ztexpzl_EJgFQSkWez) | 2019 |\n| 40. | **Deep Learning and Bayesian Methods** | Lots of Legends, HSE Moscow | [DLBM-SS](http:\u002F\u002Fdeepbayes.ru\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLe5rNUydzV9QHe8VDStpU0o8Yp63OecdW) | 2019 |\n| | | | | | |\n| 41. | **The Mathematics of Deep Learning and Data Science** | Lots of Legends, Isaac Newton Institute, Cambridge | [MoDL-DS](https:\u002F\u002Fgateway.newton.ac.uk\u002Fevent\u002Fofbw46) | [Lecture-Videos](https:\u002F\u002Fgateway.newton.ac.uk\u002Fevent\u002Fofbw46\u002Fprogramme) | 2019 |\n| 42. | **Geometry of Deep Learning** | Lots of Legends, MSR Redmond | [GoDL](https:\u002F\u002Fwww.microsoft.com\u002Fen-us\u002Fresearch\u002Fevent\u002Fai-institute-2019) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLD7HFcN7LXRe30qq36It2XCljxc340O_d) | 2019 |\n| 43. | **Deep Learning for Science School** | Many folks, LBNL, Berkeley | [DLfSS](https:\u002F\u002Fdl4sci-school.lbl.gov\u002Fagenda) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL20S5EeApOSvfvEyhCPOUzU7zkBcR5-eL) | 2019 |\n| 44. | **Emerging Challenges in Deep Learning** | Lots of Legends, Simons Institute, Berkeley | [ECDL](https:\u002F\u002Fsimons.berkeley.edu\u002Fworkshops\u002Fschedule\u002F10629) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLgKuh-lKre10BpafDrv0fg2VNUweWXWVd) | 2019 |\n| 45. | **Full Stack Deep Learning** | Pieter Abbeel and many others, UC Berkeley | [FSDL-M19](https:\u002F\u002Ffullstackdeeplearning.com\u002Fmarch2019) | [YouTube-Lectures-Day-1](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL1T8fO7ArWlcf3Hc4VMEVBlH8HZm_NbeB) \u003Cbr\u002F> [Day-2](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL1T8fO7ArWlf6TWwdstb-PcwlubnlrKrm) | 2019 |\n| 46. | **Algorithmic and Theoretical aspects of Machine Learning** | Lots of legends, IIIT-Bengaluru | [ACM-ML](https:\u002F\u002Findia.acm.org\u002Feducation\u002Fmachine-learning) \u003Cbr\u002F> [nptel](https:\u002F\u002Fnptel.ac.in\u002Fcourses\u002F128\u002F106\u002F128106011\u002F) | [YouTube-Lectures](https:\u002F\u002Fnptel.ac.in\u002Fcourses\u002F128\u002F106\u002F128106011) | 2019 |\n| 47. | **Deep Learning and Reinforcement Learning Summer School** | Lots of Legends, AMII, Edmonton, Canada | [DLRL-2019](https:\u002F\u002Fdlrlsummerschool.ca\u002Fpast-years) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLKlhhkvvU8-aXmPQZNYG_e-2nTd0tJE8v) | 2019 |\n| 48. | **Mathematics of Machine Learning** - Summer Graduate School | Lots of Legends, University of Washington | [MoML-SGS](http:\u002F\u002Fwww.msri.org\u002Fsummer_schools\u002F866#schedule), [MoML-SS](http:\u002F\u002Fmathofml.cs.washington.edu\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLTPQEx-31JXhguCush5J7OGnEORofoCW9) | 2019 |\n| 49. | **Workshop on Theory of Deep Learning: Where next?** | Lots of Legends, IAS, Princeton University | [WTDL](https:\u002F\u002Fwww.math.ias.edu\u002Fwtdl) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLdDZb3TwJPZ5dqqg_S-rgJqSFeH4DQqFQ) | 2019 |\n| 50. | **Computational Vision Summer School** | Lots of Legends, Black Forest, Germany | [CVSS-2019](http:\u002F\u002Forga.cvss.cc\u002Fprogram-cvss-2019\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLeCNfJWZKqxsvidOlVLtWq9s7sIsX1QTC) | 2019 |\n| | | | | | |\n| 51. | **Learning under complex structure** | Lots of Legends, MIT | [LUCS](https:\u002F\u002Fmifods.mit.edu\u002Fcomplex.php) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLM4Pv4KYYzGwhIHcaY6zYR7M9hhFO4Vud) | 2020 |\n| 52. | **Machine Learning Summer School** | Lots of Legends, MPI-IS Tübingen (virtual) | [MLSS](http:\u002F\u002Fmlss.tuebingen.mpg.de\u002F2020\u002Fschedule.html) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCBOgpkDhQuYeVVjuzS5Wtxw\u002Fvideos) | SS2020 |\n| 53. | **Eastern European Machine Learning Summer School** | Lots of Legends, Kraków, Poland (virtual) | [EEML](https:\u002F\u002Fwww.eeml.eu\u002Fprogram) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLaKY4p4V3gE1j01FOY2FeglV4jRntQj84) | S2020 |\n| 54. | **Lisbon Machine Learning Summer School** | Lots of Legends, Lisbon, Portugal (virtual) | [LxMLS](http:\u002F\u002Flxmls.it.pt\u002F2020\u002F?page_id=19) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCkVFZWgT1jR75UvSLGP9_mw) | S2020 |\n| 55. | **Workshop on New Directions in Optimization, Statistics and Machine Learning** | Lots of Legends, Institute of Advanced Study, Princeton | [ML-Opt new dir.](https:\u002F\u002Fwww.ias.edu\u002Fvideo\u002Fworkshop\u002F2020\u002F0415-16) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLdDZb3TwJPZ4Ri6i0MIdesIEpYK4lx17Q) | 2020 |\n| 56. | **Mediterranean Machine Learning School** | Lots of Legends, Italy (virtual) | [M2L-school](https:\u002F\u002Fwww.m2lschool.org\u002Ftalks) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLF-wkqRv4u1YRbfnwN8cXXyrmXld-sked) | 2021 |\n| 57. | **Mathematics of Machine Learning - One World Seminar** | Lots of Legends, Virtual | [1W-ML](https:\u002F\u002Fsites.google.com\u002Fview\u002Foneworldml\u002Fpast-events) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCz7WlgXs20CzugkfxhFCNFg\u002Fvideos) | 2020 - now |\n| 58. | **Deep Learning Theory Summer School** | Lots of Legends, Princeton University (virtual) | [DLT'21](https:\u002F\u002Fdeep-learning-summer-school.princeton.edu) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL2mB9GGlueJj_FNjJ8RWgz4Nc_hCSXfMU) | 2021 |\n| | | | | | |\n\n[Go to Contents :arrow_heading_up:](https:\u002F\u002Fgithub.com\u002Fkmario23\u002Fdeep-learning-drizzle#contents)\n\n:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:\n### :bird: Bird's Eye view of A(G)I :eagle:\n:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:\n\n| S.No | Course Name | University\u002FInstructor(s) | Course WebPage | Lecture Videos | Year |\n| ---- | -------------------------------------- | -------------------------------------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | --------- |\n| 1. | **Artificial General Intelligence** | Lots of Legends, MIT | [6.S099-AGI](https:\u002F\u002Fagi.mit.edu\u002F) | [Lecture-Videos](https:\u002F\u002Fagi.mit.edu\u002F) | 2018-2019 |\n| 2. | **AI Podcast** | Lots of Legends, MIT | [AI-Pod](https:\u002F\u002Flexfridman.com\u002Fai\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLrAXtmErZgOdP_8GztsuKi9nrraNbKKp4) | 2018-2019 |\n| 3. | **NYU - AI Seminars** | Lots of Legends, NYU | [modern-AI](https:\u002F\u002Fengineering.nyu.edu\u002Facademics\u002Fdepartments\u002Felectrical-and-computer-engineering\u002Fece-seminar-series\u002Fmodern-artificial) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLhwo5ntex8iY9xhpSwWas451NgVuqBE7U) | 2017-now |\n| 4. | **Deep Learning: Alchemy or Science?** | Lots of Legends, Institute for Advanced Study, Princeton | [DLAS](https:\u002F\u002Fvideo.ias.edu\u002Fdeeplearning\u002F2019\u002F0222) \u003Cbr\u002F> [Agenda](https:\u002F\u002Fwww.math.ias.edu\u002Ftml\u002Fdlasagenda) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLdDZb3TwJPZ7aAxhIHALBoh8l6-UxmMNP) | 2019 |\n| | | | | | |\n\n[Go to Contents :arrow_heading_up:](https:\u002F\u002Fgithub.com\u002Fkmario23\u002Fdeep-learning-drizzle#contents)\n\n:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:\n### To-Do :running:\n:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:\n\n:white_large_square: Optimization courses which form the foundation for ML, DL, RL\n\n:white_large_square: Computer Vision courses which are DL & ML heavy\n\n:white_large_square: Speech recognition courses which are DL heavy\n\n:white_large_square: Structured Courses on Geometric, Graph Neural Networks\n\n:white_large_square: Section on Autonomous Vehicles\n\n:white_large_square: Section on Computer Graphics with ML\u002FDL focus\n\n:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:\n\n[Go to Contents :arrow_heading_up:](https:\u002F\u002Fgithub.com\u002Fkmario23\u002Fdeep-learning-drizzle#contents) \n\n:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:\n\n\n### Around the Web :earth_asia:\n\n - [Montreal.AI](http:\u002F\u002Fwww.montreal.ai\u002Fai4all.pdf)\n - [UPC-DLAI-2018](https:\u002F\u002Ftelecombcn-dl.github.io\u002F2018-dlai\u002F)\n - [UPC-DLAI-2019](https:\u002F\u002Ftelecombcn-dl.github.io\u002Fdlai-2019\u002F)\n - [www.hashtagtechgeek.com](https:\u002F\u002Fwww.hashtagtechgeek.com\u002F2019\u002F10\u002F250-machine-learning-deep-learning-videos-courseware.html)\n - [UPC-Barcelona, IDL-2020](https:\u002F\u002Ftelecombcn-dl.github.io\u002Fidl-2020\u002F) \n - [UPC-DLAI-2020](https:\u002F\u002Ftelecombcn-dl.github.io\u002Fdlai-2020) \n\n:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:\n\n\n### Contributions :pray:\n\nIf you find a course that fits in any of the above categories (i.e. DL, ML, RL, CV, NLP), **and** the course has lecture videos (with slides being optional), then please raise an issue or send a PR by updating the course according to the above format.\n\n\n:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:\n\n\n### Support :moneybag:\n\n**Optional:** If you're a kind Samaritan and want to support me, please do so if possible, for which I would eternally be thankful and, most importantly, your contribution imbues me with greater motivation to work, particularly in hard times :pray:\n\n[![](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fkmario23_deep-learning-drizzle_readme_3ed0eaf21fb2.gif)](https:\u002F\u002Fwww.paypal.com\u002Fcgi-bin\u002Fwebscr?cmd=_s-xclick&hosted_button_id=NT3EATS5N35WU)\n\n\nVielen lieben Dank! :blue_heart: \n\n:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:\n\n### :gift_heart: :mortar_board: :mortar_board: :mortar_board: :mortar_board: :mortar_board: :mortar_board: :mortar_board::mortar_board: :mortar_board: :mortar_board: :mortar_board: :mortar_board: :mortar_board: :mortar_board: :mortar_board: :mortar_board: :mortar_board: :mortar_board: :mortar_board: :mortar_board: :mortar_board::mortar_board: :mortar_board: :mortar_board: :mortar_board: :mortar_board: :mortar_board: :mortar_board: :gift_heart: \n\n:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:\n\n","# :balloon: :tada: 深度学习细雨 :confetti_ball: :balloon:\n\n:books: [**“多读一些，直到你开始培养直觉，然后相信你的直觉，大胆去做吧！”** ](https:\u002F\u002Fwww.deeplearning.ai\u002Fhodl-geoffrey-hinton\u002F) :books: ​\u003Cbr\u002F> 多伦多大学杰弗里·辛顿教授\n\n:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign……（此处省略大量减号符号）\n\n### 目录\n:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign……\n\n| | |\n| ------------------------------------------------------------ | ------------------------------------------------------------ |\n| **深度学习（深度神经网络）** [:arrow_heading_down:](https:\u002F\u002Fgithub.com\u002Fkmario23\u002Fdeep-learning-drizzle#tada-deep-learning-deep-neural-networks-confetti_ball-balloon) | **概率图模型** [:arrow_heading_down:](https:\u002F\u002Fgithub.com\u002Fkmario23\u002Fdeep-learning-drizzle#loudspeaker-probabilistic-graphical-models-sparkles) |\n| | |\n| **机器学习基础** [:arrow_heading_down:](https:\u002F\u002Fgithub.com\u002Fkmario23\u002Fdeep-learning-drizzle#cupid-machine-learning-fundamentals-cyclone-boom) | **自然语言处理** [:arrow_heading_down:](https:\u002F\u002Fgithub.com\u002Fkmario23\u002Fdeep-learning-drizzle#hibiscus-natural-language-processing-cherry_blossom-sparkling_heart) |\n| | |\n| **机器学习中的优化** [:arrow_heading_down:](https:\u002F\u002Fgithub.com\u002Fkmario23\u002Fdeep-learning-drizzle#cupid-optimization-for-machine-learning-cyclone-boom) | **自动语音识别** [:arrow_heading_down:](https:\u002F\u002Fgithub.com\u002Fkmario23\u002Fdeep-learning-drizzle#speaking_head-automatic-speech-recognition-speech_balloon-thought_balloon) |\n| | |\n| **通用机器学习** [:arrow_heading_down:](https:\u002F\u002Fgithub.com\u002Fkmario23\u002Fdeep-learning-drizzle#cupid-general-machine-learning-cyclone-boom) | **现代计算机视觉** [:arrow_heading_down:](https:\u002F\u002Fgithub.com\u002Fkmario23\u002Fdeep-learning-drizzle#fire-modern-computer-vision-camera_flash-movie_camera) |\n| | |\n| **强化学习** [:arrow_heading_down:](https:\u002F\u002Fgithub.com\u002Fkmario23\u002Fdeep-learning-drizzle#balloon-reinforcement-learning-hotsprings-video_game) | **训练营或暑期学校** [:arrow_heading_down:](https:\u002F\u002Fgithub.com\u002Fkmario23\u002Fdeep-learning-drizzle#star2-boot-camps-or-summer-schools-maple_leaf) |\n| | |\n| **贝叶斯深度学习** [:arrow_heading_down:](https:\u002F\u002Fgithub.com\u002Fkmario23\u002Fdeep-learning-drizzle#game_die-bayesian-deep-learning-spades-gem) | **医学影像** [:arrow_heading_down:](https:\u002F\u002Fgithub.com\u002Fkmario23\u002Fdeep-learning-drizzle#movie_camera-medical-imaging-camera-video_camera) |\n| | |\n| **图神经网络** [:arrow_heading_down: ](https:\u002F\u002Fgithub.com\u002Fkmario23\u002Fdeep-learning-drizzle#tada-graph-neural-networks-geometric-dl-confetti_ball-balloon) | **人工智能鸟瞰图** [:arrow_heading_down:](https:\u002F\u002Fgithub.com\u002Fkmario23\u002Fdeep-learning-drizzle#bird-birds-eye-view-of-agi-eagle) |\n| | |\n\n:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign……（此处省略大量减号符号）\n\n## :tada: 深度学习（深度神经网络） :confetti_ball: :balloon: \n:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign……（共64个减号）\n\n| S.No | Course Name | University\u002FInstructor(s) | Course WebPage | Lecture Videos | Year |\n| ---- | ----------------------------------------------------- | ---------------------------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | --------------- |\n| 1. | **Neural Networks for Machine Learning** | Geoffrey Hinton, University of Toronto | [Lecture-Slides](http:\u002F\u002Fwww.cs.toronto.edu\u002F~hinton\u002Fcoursera_slides.html) \u003Cbr\u002F> [CSC321-tijmen](https:\u002F\u002Fwww.cs.toronto.edu\u002F~tijmen\u002Fcsc321\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLoRl3Ht4JOcdU872GhiYWf6jwrk_SNhz9) \u003Cbr\u002F> [UofT-mirror](https:\u002F\u002Fwww.cs.toronto.edu\u002F~hinton\u002Fcoursera_lectures.html) | 2012 \u003Cbr\u002F> 2014 |\n| 2. | **Neural Networks Demystified** | Stephen Welch, Welch Labs | [Suppl. Code](https:\u002F\u002Fgithub.com\u002Fstephencwelch\u002FNeural-Networks-Demystified) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLiaHhY2iBX9hdHaRr6b7XevZtgZRa1PoU) | 2014 |\n| 3. | **Deep Learning at Oxford** | Nando de Freitas, Oxford University | [Oxford-ML](http:\u002F\u002Fwww.cs.ox.ac.uk\u002Fteaching\u002Fcourses\u002F2014-2015\u002Fml\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLE6Wd9FR--EfW8dtjAuPoTuPcqmOV53Fu) | 2015 |\n| 4. | **Deep Learning for Perception** | Dhruv Batra, Virginia Tech | [ECE-6504](https:\u002F\u002Fcomputing.ece.vt.edu\u002F~f15ece6504\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL-fZD610i7yAsfH2eLBiRDa90kL2ML0f7) | 2015 |\n| 5. | **Deep Learning** | Ali Ghodsi, University of Waterloo | [STAT-946](https:\u002F\u002Fuwaterloo.ca\u002Fdata-analytics\u002Fdeep-learning) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLehuLRPyt1Hyi78UOkMPWCGRxGcA9NVOE) | F2015 |\n| 6. | **CS231n: CNNs for Visual Recognition** | Andrej Karpathy, Stanford University | [CS231n](http:\u002F\u002Fcs231n.stanford.edu\u002F2015\u002F) | `None` | 2015 |\n| 7. | **CS224d: Deep Learning for NLP** | Richard Socher, Stanford University | [CS224d](http:\u002F\u002Fcs224d.stanford.edu) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLmImxx8Char8dxWB9LRqdpCTmewaml96q) | 2015 |\n| 8. | **Bay Area Deep Learning** | Many legends, Stanford | `None` | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLrAXtmErZgOfMuxkACrYnD2fTgbzk2THW) | 2016 |\n| 9. | **CS231n: CNNs for Visual Recognition** | Andrej Karpathy, Stanford University | [CS231n](http:\u002F\u002Fcs231n.stanford.edu\u002F2016\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLkt2uSq6rBVctENoVBg1TpCC7OQi31AlC) \u003Cbr\u002F>[(Academic Torrent)](https:\u002F\u002Facademictorrents.com\u002Fdetails\u002F46c5af9e2075d9af06f280b55b65cf9b44eb9fe7) | 2016 |\n| 10. | **Neural Networks** | Hugo Larochelle, Université de Sherbrooke | [Neural-Networks](http:\u002F\u002Finfo.usherbrooke.ca\u002Fhlarochelle\u002Fneural_networks\u002Fcontent.html) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL6Xpj9I5qXYEcOhn7TqghAJ6NAPrNmUBH) \u003Cbr\u002F> [(Academic Torrent)](https:\u002F\u002Facademictorrents.com\u002Fdetails\u002Fe046bca3bc837053d1609ef33d623ee5c5af7300) | 2016 |\n| | | | | | |\n| 11. | **CS224d: Deep Learning for NLP** | Richard Socher, Stanford University | [CS224d](http:\u002F\u002Fcs224d.stanford.edu) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLlJy-eBtNFt4CSVWYqscHDdP58M3zFHIG) \u003Cbr\u002F>[(Academic Torrent)](https:\u002F\u002Facademictorrents.com\u002Fdetails\u002Fdd9b74b50a1292b4b154094b7338ec1d66e8894d) | 2016 |\n| 12. | **CS224n: NLP with Deep Learning** | Richard Socher, Stanford University | [CS224n](http:\u002F\u002Fweb.stanford.edu\u002Fclass\u002Fcs224n\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL3FW7Lu3i5Jsnh1rnUwq_TcylNr7EkRe6) | 2017 |\n| 13. | **CS231n: CNNs for Visual Recognition** | Justin Johnson, Stanford University | [CS231n](http:\u002F\u002Fcs231n.stanford.edu\u002F2017\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL3FW7Lu3i5JvHM8ljYj-zLfQRF3EO8sYv) \u003Cbr\u002F> [(Academic Torrent)](https:\u002F\u002Facademictorrents.com\u002Fdetails\u002Fed8a16ebb346e14119a03371665306609e485f13) | 2017 |\n| 14. | **Topics in Deep Learning** | Ruslan Salakhutdinov, CMU | [10707](https:\u002F\u002Fdeeplearning-cmu-10707.github.io\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLpIxOj-HnDsOSL__Buy7_UEVQkyfhHapa) | F2017 |\n| 15. | **Deep Learning Crash Course** | Leo Isikdogan, UT Austin | `None` | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLWKotBjTDoLj3rXBL-nEIPRN9V3a9Cx07) | 2017 |\n| 16. | **Deep Learning and its Applications** | François Pitié, Trinity College Dublin | [EE4C16](https:\u002F\u002Fgithub.com\u002Ffrcs\u002F4C16-2017) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLIo1iEzl5iB9NkulNR0X5vXN8AaEKglWT) | 2017 |\n| 17. | **Deep Learning** | Andrew Ng, Stanford University | [CS230](http:\u002F\u002Fcs230.stanford.edu\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLoROMvodv4rOABXSygHTsbvUz4G_YQhOb) | 2018 |\n| 18. | **UvA Deep Learning** | Efstratios Gavves, University of Amsterdam | [UvA-DLC](https:\u002F\u002Fuvadlc.github.io\u002F) | [Lecture-Videos](https:\u002F\u002Fuvadlc.github.io\u002Flectures-sep2018.html) | 2018 |\n| 19. | **Advanced Deep Learning and Reinforcement Learning** | Many legends, DeepMind | `None` | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLqYmG7hTraZDNJre23vqCGIVpfZ_K2RZs) | 2018 |\n| 20. | **Machine Learning** | Peter Bloem, Vrije Universiteit Amsterdam | [MLVU](https:\u002F\u002Fmlvu.github.io\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLCof9EqayQgsORO3pFzeYZFz6cszYO0VJ) | 2018 |\n| | | | | | |\n| 21. | **Deep Learning** | Francois Fleuret, EPFL | [EE-59](https:\u002F\u002Ffleuret.org\u002Fee559-2018\u002Fdlc) | [Video-Lectures](https:\u002F\u002Ffleuret.org\u002Fee559-2018\u002Fdlc\u002F#materials) | 2018 |\n| 22. | **Introduction to Deep Learning** | Alexander Amini, Harini Suresh and others, MIT | [6.S191](http:\u002F\u002Fintrotodeeplearning.com\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLtBw6njQRU-rwp5__7C0oIVt26ZgjG9NI) \u003Cbr\u002F> [2017-version](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLkkuNyzb8LmxFutYuPA7B4oiMn6cjD6Rs) | 2017- 2021 |\n| 23. | **Deep Learning for Self-Driving Cars** | Lex Fridman, MIT | [6.S094](https:\u002F\u002Fselfdrivingcars.mit.edu\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLrAXtmErZgOeiKm4sgNOknGvNjby9efdf) | 2017-2018 |\n| 24. | **Introduction to Deep Learning** | Bhiksha Raj and many others, CMU | [11-485\u002F785](http:\u002F\u002Fdeeplearning.cs.cmu.edu\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLp-0K3kfddPwJBJ4Q8We-0yNQEG0fZrSa) | S2018 |\n| 25. | **Introduction to Deep Learning** | Bhiksha Raj and many others, CMU | [11-485\u002F785](http:\u002F\u002Fdeeplearning.cs.cmu.edu\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLp-0K3kfddPyH44FP0dl0CbYprvTcfgOI) [Recitation-Inclusive](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLLR0_ZOlbfD6KDBq93G8-guHI-J1ICeFm) | F2018 |\n| 26. | **Deep Learning Specialization** | Andrew Ng, Stanford | [DL.AI](https:\u002F\u002Fwww.deeplearning.ai\u002Fdeep-learning-specialization\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCcIXc5mJsHVYTZR1maL5l9w\u002Fplaylists) | 2017-2018 |\n| 27. | **Deep Learning** | Ali Ghodsi, University of Waterloo | [STAT-946](https:\u002F\u002Fuwaterloo.ca\u002Fdata-analytics\u002Fteaching\u002Fdeep-learning-2017) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLehuLRPyt1HxTolYUWeyyIoxDabDmaOSB) | F2017 |\n| 28. | **Deep Learning** | Mitesh Khapra, IIT-Madras | [CS7015](https:\u002F\u002Fwww.cse.iitm.ac.in\u002F~miteshk\u002FCS7015.html) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLyqSpQzTE6M9gCgajvQbc68Hk_JKGBAYT) | 2018 |\n| 29. | **Deep Learning for AI** | UPC Barcelona | [DLAI-2017](https:\u002F\u002Ftelecombcn-dl.github.io\u002F2017-dlai\u002F) \u003Cbr\u002F> [DLAI-2018](https:\u002F\u002Ftelecombcn-dl.github.io\u002F2018-dlai\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL-5eMc3HQTBagIUjKefjcTbnXC0wXC_vd) | 2017-2018 |\n| 30. | **Deep Learning** | Alex Bronstein and Avi Mendelson, Technion | [CS236605](https:\u002F\u002Fvistalab-technion.github.io\u002Fcs236605\u002Finfo\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLM0a6Z788YAZuqg2Ip-_dPLzEd33lZvP2) | 2018 |\n| | | | | | |\n| 31. | **MIT Deep Learning** | Many Researchers, Lex Fridman, MIT | [6.S094, 6.S091, 6.S093](https:\u002F\u002Fdeeplearning.mit.edu\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLrAXtmErZgOeiKm4sgNOknGvNjby9efdf) | 2019 |\n| 32. | **Deep Learning Book** companion videos | Ian Goodfellow and others | [DL-book slides](https:\u002F\u002Fwww.deeplearningbook.org\u002Flecture_slides.html) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLsXu9MHQGs8df5A4PzQGw-kfviylC-R9b) | 2017 |\n| 33. | **Theories of Deep Learning** | Many Legends, Stanford | [Stats-385](https:\u002F\u002Fstats385.github.io\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLwUqqMt5en7fFLwSDa9V3JIkDam-WWgqy) \u003Cbr\u002F> (first 10 lectures) | F2017 |\n| 34. | **Neural Networks** | Grant Sanderson | `None` | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi) | 2017-2018 |\n| 35. | **CS230: Deep Learning** | Andrew Ng, Kian Katanforoosh, Stanford | [CS230](http:\u002F\u002Fcs230.stanford.edu\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLoROMvodv4rOABXSygHTsbvUz4G_YQhOb) | A2018 |\n| 36. | **Theory of Deep Learning** | Lots of Legends, Canary Islands | [DALI'18](http:\u002F\u002Fdalimeeting.org\u002Fdali2018\u002FworkshopTheoryDL.html) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLeCNfJWZKqxtWBnV8gefGqmmPgz9YF4LR) | 2018 |\n| 37. | **Introduction to Deep Learning** | Alex Smola, UC Berkeley | [Stat-157](http:\u002F\u002Fcourses.d2l.ai\u002Fberkeley-stat-157\u002Findex.html) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLZSO_6-bSqHQHBCoGaObUljoXAyyqhpFW) | S2019 |\n| 38. | **Deep Unsupervised Learning** | Pieter Abbeel, UC Berkeley | [CS294-158](https:\u002F\u002Fsites.google.com\u002Fview\u002Fberkeley-cs294-158-sp19\u002Fhome) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCf4SX8kAZM_oGcZjMREsU9w\u002Fvideos) | S2019 |\n| 39. | **Machine Learning** | Peter Bloem, Vrije Universiteit Amsterdam | [MLVU](https:\u002F\u002Fmlvu.github.io\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLCof9EqayQgupldnTvqNy_BThTcME5r93) | 2019 |\n| 40. | **Deep Learning on Computational Accelerators** | Alex Bronstein and Avi Mendelson, Technion | [CS236605](https:\u002F\u002Fvistalab-technion.github.io\u002Fcs236605\u002Flectures\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLM0a6Z788YAa_WCy_V-q9NrGm5qQegZR5) | S2019 |\n| | | | | | |\n| 41. | **Introduction to Deep Learning** | Bhiksha Raj and many others, CMU | [11-785](http:\u002F\u002Fwww.cs.cmu.edu\u002F~bhiksha\u002Fcourses\u002Fdeeplearning\u002FSpring.2019\u002Fwww) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLp-0K3kfddPzNdZPX4p0lVi6AcDXBofuf) | S2019 |\n| 42. | **Introduction to Deep Learning** | Bhiksha Raj and many others, CMU | [11-785](https:\u002F\u002Fwww.cs.cmu.edu\u002F~bhiksha\u002Fcourses\u002Fdeeplearning\u002FFall.2019\u002Fwww) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLp-0K3kfddPwz13VqV1PaMXF6V6dYdEsj) \u003Cbr> [Recitations](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLp-0K3kfddPxf4T59JEQKv5UanLPVsxzz) | F2019 |\n| 43. | **UvA Deep Learning** | Efstratios Gavves, University of Amsterdam | [UvA-DLC](https:\u002F\u002Fuvadlc.github.io\u002F) | [Lecture-Videos](https:\u002F\u002Fuvadlc.github.io\u002Flectures-apr2019.html) | S2019 |\n| 44. | **Deep Learning** | Prabir Kumar Biswas, IIT Kgp | `None` | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLbRMhDVUMngc7NM-gDwcBzIYZNFSK2N1a) | 2019 |\n| 45. | **Deep Learning and its Applications** | Aditya Nigam, IIT Mandi | [CS-671](http:\u002F\u002Ffaculty.iitmandi.ac.in\u002F~aditya\u002Fcs671\u002Findex.html) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLKvX2d3IUq586Ic9gIhZj6ubpWV-OJfl4) | 2019 |\n| 46. | **Neural Networks** | Neil Rhodes, Harvey Mudd College | [CS-152](https:\u002F\u002Fwww.cs.hmc.edu\u002F~rhodes\u002Fcs152\u002Fschedule.html) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLgEuVSRbAI9UIQSHGy4l01laA_12YOqEj) | F2019 |\n| 47. | **Deep Learning** | Thomas Hofmann, ETH Zürich | [DAL-DL](http:\u002F\u002Fwww.da.inf.ethz.ch\u002Fteaching\u002F2019\u002FDeepLearning) | [Lecture-Videos](https:\u002F\u002Fvideo.ethz.ch\u002Flectures\u002Fd-infk\u002F2019\u002Fautumn\u002F263-3210-00L.html) | F2019 |\n| 48. | **Deep Learning** | Milan Straka, Charles University | [NPFL114](https:\u002F\u002Fufal.mff.cuni.cz\u002Fcourses\u002Fnpfl114) | [Lecture-Videos](https:\u002F\u002Fufal.mff.cuni.cz\u002Fcourses\u002Fnpfl114\u002F1718-summer) | S2019 |\n| 49. | **UvA Deep Learning** | Efstratios Gavves, University of Amsterdam | [UvA-DLC-19](https:\u002F\u002Fuvadlc.github.io\u002F#lectures) | [Lecture-Videos](https:\u002F\u002Fuvadlc.github.io\u002F#lectures) | F2019 |\n| 50. | **Artificial Intelligence: Principles and Techniques** | Percy Liang and Dorsa Sadigh, Stanford University | [CS221](https:\u002F\u002Fstanford-cs221.github.io\u002Fautumn2019\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLoROMvodv4rO1NB9TD4iUZ3qghGEGtqNX) | F2019 |\n| | | | | | |\n| 51. | **Analyses of Deep Learning** | Lots of Legends, Stanford University | [STATS-385](https:\u002F\u002Fstats385.github.io\u002F) | [YouTube-Lectures](https:\u002F\u002Fstats385.github.io\u002Flecture_videos) | 2017-2019 |\n| 52. | **Deep Learning Foundations and Applications** | Debdoot Sheet and Sudeshna Sarkar, IIT-Kgp | [AI61002](http:\u002F\u002Fwww.facweb.iitkgp.ac.in\u002F~debdoot\u002Fcourses\u002FAI61002\u002FSpr2020) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL_AdDfjIMo6pZfwjZ0rJlkE_MIsmRW7Mh) | S2020 |\n| 53. | **Designing, Visualizing, and Understanding Deep Neural Networks** | John Canny, UC Berkeley | [CS 182\u002F282A](https:\u002F\u002Fbcourses.berkeley.edu\u002Fcourses\u002F1487769\u002Fpages\u002Fcs-l-w-182-slash-282a-designing-visualizing-and-understanding-deep-neural-networks-spring-2020) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLkFD6_40KJIwaO6Eca8kzsEFBob0nFvwm) | S2020 |\n| 54. | **Deep Learning** | Yann LeCun and Alfredo Canziani, NYU | [DS-GA 1008](https:\u002F\u002Fatcold.github.io\u002Fpytorch-Deep-Learning\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLLHTzKZzVU9eaEyErdV26ikyolxOsz6mq) | S2020 |\n| 55. | **Introduction to Deep Learning** | Bhiksha Raj, CMU | [11-785](https:\u002F\u002Fdeeplearning.cs.cmu.edu\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLp-0K3kfddPzCnS4CqKphh-zT3aDwybDe) | S2020 |\n| 56. | **Deep Unsupervised Learning** | Pieter Abbeel, UC Berkeley | [CS294-158](https:\u002F\u002Fsites.google.com\u002Fview\u002Fberkeley-cs294-158-sp20) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLwRJQ4m4UJjPiJP3691u-qWwPGVKzSlNP) | S2020 |\n| 57. | **Machine Learning** | Peter Bloem, Vrije Universiteit Amsterdam | [VUML](https:\u002F\u002Fmlvu.github.io\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLCof9EqayQgthR7IViXkAkUwel_rhxGYM) | S2020 |\n| 58. | **Deep Learning (with PyTorch)** | Alfredo Canziani and Yann LeCun, NYU | [DS-GA 1008](https:\u002F\u002Fatcold.github.io\u002Fpytorch-Deep-Learning\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLLHTzKZzVU9eaEyErdV26ikyolxOsz6mq) | S2020 |\n| 59. | **Introduction to Deep Learning and Generative Models** | Sebastian Raschka, UW-Madison | [Stat453](http:\u002F\u002Fpages.stat.wisc.edu\u002F~sraschka\u002Fteaching\u002Fstat453-ss2020\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLTKMiZHVd_2JkR6QtQEnml7swCnFBtq4P) | S2020 |\n| 60. | **Deep Learning** | Andreas Maier, FAU Erlangen-Nürnberg | [DL-2020](https:\u002F\u002Fwww.video.uni-erlangen.de\u002Fcourse\u002Fid\u002F925) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLpOGQvPCDQzvgpD3S0vTy7bJe2pf_yJFj) \u003Cbr\u002F>[Lecture-Videos](https:\u002F\u002Fwww.video.uni-erlangen.de\u002Fcourse\u002Fid\u002F925) | SS2020 |\n| | | | | | |\n| 61. | **Introduction to Deep Learning** | Laura Leal-Taixé and Matthias Niessner, TU-München | [I2DL-IN2346](https:\u002F\u002Fdvl.in.tum.de\u002Fteaching\u002Fi2dl-ss20\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLQ8Y4kIIbzy_OaXv86lfbQwPHSomk2o2e) | SS2020 |\n| 62. | **Deep Learning** | Sargur Srihari, SUNY-Buffalo | [CSE676](https:\u002F\u002Fcedar.buffalo.edu\u002F~srihari\u002FCSE676\u002F) | [YouTube-Lectures-P1](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLmx4utxjUQD70k_NzeiSIXf30m54T_e1h) \u003Cbr\u002F>[YouTube-Lectures-P2](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCUm7yUmVJyAbYh_0ppJ4H-g\u002Fvideos) | 2020 |\n| 63. | **Deep Learning Lecture Series** | Lots of Legends, DeepMind x UCL, London | [DLLS-20](https:\u002F\u002Fdeepmind.com\u002Flearning-resources\u002Fdeep-learning-lecture-series-2020) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLqYmG7hTraZCDxZ44o4p3N5Anz3lLRVZF) | 2020 |\n| 64. | **MultiModal Machine Learning** | Louis-Philippe Morency & others, Carnegie Mellon University | [11-777 MMML-20](https:\u002F\u002Fcmu-multicomp-lab.github.io\u002Fmmml-course\u002Ffall2020) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCqlHIJTGYhiwQpNuPU5e2gg\u002Fvideos) | F2020 |\n| 65. | **Reliable and Interpretable Artificial Intelligence** | Martin Vechev, ETH Zürich | [RIAI-20](https:\u002F\u002Fwww.sri.inf.ethz.ch\u002Fteaching\u002Friai2020) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLWjm4hHpaNg6c-W7JjNYDEC_kJK9oSp0Y) | F2020 |\n| 66. | **Fundamentals of Deep Learning** | David McAllester, Toyota Technological Institute, Chicago | [TTIC-31230](https:\u002F\u002Fmcallester.github.io\u002Fttic-31230\u002FFall2020) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCciVrtrRR3bQdaGbti9-hVQ\u002Fvideos) | F2020 |\n| 67. | **Foundations of Deep Learning** | Soheil Feize, University of Maryland, College Park | [CMSC 828W](http:\u002F\u002Fwww.cs.umd.edu\u002Fclass\u002Ffall2020\u002Fcmsc828W) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLHgjs9ncvHi80UCSlSvQe-TK_uOyDv_Jf) | F2020 |\n| 68. | **Deep Learning** | Andreas Geiger, Universität Tübingen | [DL-UT](https:\u002F\u002Funi-tuebingen.de\u002Ffakultaeten\u002Fmathematisch-naturwissenschaftliche-fakultaet\u002Ffachbereiche\u002Finformatik\u002Flehrstuehle\u002Fautonomous-vision\u002Fteaching\u002Flecture-deep-learning\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL05umP7R6ij3NTWIdtMbfvX7Z-4WEXRqD) | W20\u002F21 |\n| 69. | **Deep Learning** | Andreas Maier, FAU Erlangen-Nürnberg | [DL-FAU](https:\u002F\u002Fwww.fau.tv\u002Fcourse\u002Fid\u002F1599) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLpOGQvPCDQzvJEPFUQ3mJz72GJ95jyZTh) | W20\u002F21 |\n| 70. | **Fundamentals of Deep Learning** | Terence Parr and Yannet Interian, University of San Francisco | [DL-Fundamentals](https:\u002F\u002Fgithub.com\u002Fparrt\u002Ffundamentals-of-deep-learning) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLFCc_Fc116ikeol9CZcWWKqmrJljxhE4N) | S2021 |\n| | | | | | |\n| 71. | **Full Stack Deep Learning** | Pieter Abbeel, Sergey Karayev, UC Berkeley | [FS-DL](https:\u002F\u002Ffullstackdeeplearning.com\u002Fspring2021) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL1T8fO7ArWlcWg04OgNiJy91PywMKT2lv) | S2021 |\n| 72. | **Deep Learning: Designing, Visualizing, and Understanding DNNs** | Sergey Levine, UC Berkeley | [CS 182](https:\u002F\u002Fcs182sp21.github.io) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL_iWQOsE6TfVmKkQHucjPAoRtIJYt8a5A) | S2021 |\n| 73. | **Deep Learning in the Life Sciences** | Manolis Kellis, MIT | [6.874](https:\u002F\u002Fmit6874.github.io) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLypiXJdtIca5sxV7aE3-PS9fYX3vUdIOX) | S2021 |\n| 74. | **Introduction to Deep Learning and Generative Models** | Sebastian Raschka, University of Wisconsin-Madison | [Stat 453](http:\u002F\u002Fpages.stat.wisc.edu\u002F~sraschka\u002Fteaching\u002Fstat453-ss2021) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLTKMiZHVd_2KJtIXOW0zFhFfBaJJilH51) | S2021 |\n| 75. | **Deep Learning** | Alfredo Canziani and Yann LeCun, NYU | [NYU-DLSP21](https:\u002F\u002Fatcold.github.io\u002FNYU-DLSP21) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLLHTzKZzVU9e6xUfG10TkTWApKSZCzuBI) | S2021 |\n| 76. | **Applied Deep Learning** | Alexander Pacha, TU Wien | `None` | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLNsFwZQ_pkE8xNYTEyorbaWPN7nvbWyk1) | 2020-2021 |\n| 77. | **Machine Learning** | Hung-yi Lee, National Taiwan University | [ML'21](https:\u002F\u002Fspeech.ee.ntu.edu.tw\u002F~hylee\u002Fml\u002F2021-spring.php) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLJV_el3uVTsNxV_IGauQZBHjBKZ26JHjd) | S2021 |\n| 78. | **Mathematics of Deep Learning** | Lots of legends, FAU | [MoDL](https:\u002F\u002Fwww.fau.tv\u002Fcourse\u002Fid\u002F878) | [Lecture-Videos](https:\u002F\u002Fwww.fau.tv\u002Fcourse\u002Fid\u002F878) | 2019-21 |\n| 79. | **Deep Learning** | Peter Bloem, Michael Cochez, and Jakub Tomczak, VU-Amsterdam | [DL](https:\u002F\u002Fdlvu.github.io\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCYh1zKnwzrSjrO2Ae-akfTg\u002Fplaylists) | 2020-21 |\n| 80. | **Applied Deep Learning** | Maziar Raissi, UC Boulder | [ADL'21](https:\u002F\u002Fgithub.com\u002Fmaziarraissi\u002FApplied-Deep-Learning) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLoEMreTa9CNmuxQeIKWaz7AVFd_ZeAcy4) | 2021 |\n| | | | | | |\n| 81. | **An Introduction to Group Equivariant Deep Learning** | Erik J. Bekkers, Universiteit van Amsterdam | [UvAGEDL](https:\u002F\u002Fuvagedl.github.io) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL8FnQMH2k7jzPrxqdYufoiYVHim8PyZWd) | 2022 |\n| | | | | | |\n\n[前往目录 :arrow_heading_up:](https:\u002F\u002Fgithub.com\u002Fkmario23\u002Fdeep-learning-drizzle#contents) \n\n:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::...（省略号表示重复）:heavy_minus_sign::heavy_minus_sign:\n\n### :cupid: 机器学习基础 :cyclone: :boom: \n:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::...（省略号表示重复）:heavy_minus_sign::heavy_minus_sign:\n\n| 序号 | 课程名称 | 大学\u002F讲师 | 课程网页 | 视频讲座 | 年份 |\n| ---- | ------------------------------------------------------------ | ------------------------------------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ---------- |\n| 1. | **线性代数** | 吉尔伯特·斯特兰格，麻省理工学院 | [18.06 SC](http:\u002F\u002Focw.mit.edu\u002F18-06SCF11) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL221E2BBF13BECF6C) | 2011 |\n| 2. | **概率入门** | 杰弗里·米勒，布朗大学 | `mathematical monk` | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL17567A1A3F5DB5E4) | 2011 |\n| 3. | **信息论、模式识别与神经网络** | 戴维·麦凯，剑桥大学 | [ITPRNN](http:\u002F\u002Fwww.inference.org.uk\u002Fmackay\u002Fitprnn) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLruBu5BI5n4aFpG32iMbdWoRVAA-Vcso6) | 2012 |\n| 4. | **线性代数复习** | 齐科·科尔特，卡内基梅隆大学 | [LinAlg](http:\u002F\u002Fwww.cs.cmu.edu\u002F~zkolter\u002Fcourse\u002Flinalg\u002Findex.html) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLM4Pv4KYYzGzL5ay6dmpyzRnbzQ__8v_t) | 2013 |\n| 5. | **概率与统计** | 米歇尔·范·比曾 | `无` | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLX2gX-ftPVXUWwTzAkOhBdhplvz0fByqV) | 2015 |\n| 6. | **线性代数：深入介绍** | 帕维尔·格林菲尔德 | `无` | [第1部分](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLlXfTHzgMRUKXD88IdzS14F4NxAZudSmv) \u003Cbr\u002F> [第2部分](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLlXfTHzgMRULWJYthculb2QWEiZOkwTSU) \u003Cbr\u002F> [第3部分](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLlXfTHzgMRUIqYrutsFXCOmiqKUgOgGJ5) \u003Cbr\u002F> [第4部分](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLlXfTHzgMRULZfrNCrrJ7xDcTjGr633mm) | 2015-2017 |\n| 7. | **多元微积分** | 格兰特·桑德森，可汗学院 | `无` | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLSQl0a2vh4HC5feHa6Rc5c0wbRTx56nF7) | 2016 |\n| 8. | **线性代数的本质** | 格兰特·桑德森 | `无` | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLZHQObOWTQDMsr9K-rj53DwVRMYO3t5Yr) | 2016 |\n| 9. | **微积分的本质** | 格兰特·桑德森 | `无` | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLZHQObOWTQDMsr9K-rj53DwVRMYO3t5Yr) | 2017-2018 |\n| 10. | **机器学习的数学基础** | 杰夫·戈登，卡内基梅隆大学 | [10-606](https:\u002F\u002Fcanvas.cmu.edu\u002Fcourses\u002F603\u002Fassignments\u002Fsyllabus), [10-607](https:\u002F\u002Fpiazza.com\u002Fcmu\u002Ffall2017\u002F1060610607\u002Fhome) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL7y-1rk2cCsAqRtWoZ95z-GMcecVG5mzA) | F2017 |\n| | | | | | |\n| 11. | **机器学习的数学**（线性代数、微积分） | 大卫·戴伊、塞缪尔·库珀和弗雷迪·佩奇，IC伦敦 | [MML](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Flinear-algebra-machine-learning) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLmAuaUS7wSOP-iTNDivR0ANKuTUhEzMe4) | 2018 |\n| 12. | **多元微积分** | S.K. 古普塔和桑杰夫·库马尔，印度理工学院鲁尔基校区 | [MVC](https:\u002F\u002Fnptel.ac.in\u002Fsyllabus\u002F111107108\u002F) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLq-Gm0yRYwTiQtK374NzhFOcQkWmJ71vx) | 2018 |\n| 13. | **工程概率论** | 瑞奇·拉德克，伦斯勒理工学院 | `无` | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLuh62Q4Sv7BU1dN2G6ncyiMbML7OXh_Jx) | 2018 |\n| 14. | **数据分析、信号处理与机器学习中的矩阵方法** | 吉尔伯特·斯特兰格，麻省理工学院 | [18.065](https:\u002F\u002Focw.mit.edu\u002Fcourses\u002Fmathematics\u002F18-065-matrix-methods-in-data-analysis-signal-processing-and-machine-learning-spring-2018) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLUl4u3cNGP61iQEFiWLE21EJCxwmWvvek) | S2018 |\n| 15. | **信息论** | 希曼舒·泰亚吉，IISC班加罗尔 | [E2 201](https:\u002F\u002Fece.iisc.ac.in\u002F~htyagi\u002Fcourse-E2201-2020.html) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLgMDNELGJ1CYS-8dlMGPIaowVfeda4nUj) | 2018-20 |\n| 16. | **数学营** | 马克·沃克，亚利桑那大学 | [UAMathCamp \u002F Econ-519](http:\u002F\u002Fwww.u.arizona.edu\u002F~mwalker\u002FMathCamp2019.htm) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLcjqUUQt__ZGLhwUacPm7_RKs2eJNFwco) | 2019 |\n| 17. | **线性代数的2020愿景** | 吉尔伯特·斯特兰格，麻省理工学院 | [VoLA](https:\u002F\u002Focw.mit.edu\u002Fresources\u002Fres-18-010-a-2020-vision-of-linear-algebra-spring-2020\u002F) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLUl4u3cNGP61iQEFiWLE21EJCxwmWvvek) | S2020 |\n| 18. | **数值计算与机器学习的数学** | 西蒙·鲁辛凯维奇，普林斯顿大学 | [COS-302](https:\u002F\u002Fwww.cs.princeton.edu\u002Fcourses\u002Farchive\u002Ffall20\u002Fcos302\u002Foutline.html) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL88aSuXxl_dSjC5pIG8bGkC5wsUPyW_Hh) | F2020 |\n| 19. | **神经科学家必备统计学** | 菲利普·贝伦斯，图宾根大学医院 | `无` | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL05umP7R6ij0Gw5SLIrOA1dMYScCx4oXT) | 2020 |\n| 20. | **机器学习的数学** | 乌尔丽克·冯·卢克斯堡，埃伯哈德·卡尔斯图宾根大学 | [Math4ML](https:\u002F\u002Fwww.tml.cs.uni-tuebingen.de\u002Fteaching\u002F2020_maths_for_ml) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL05umP7R6ij1a6KdEy8PVE9zoCv6SlHRS) | W2020 |\n| 21. | **因果推断导论** | 布雷迪·尼尔，Mila，蒙特利尔 | [CausalInf](https:\u002F\u002Fwww.bradyneal.com\u002Fcausal-inference-course) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLoazKTcS0Rzb6bb9L508cyJ1z-U9iWkA0) | F2020 |\n| 22. | **应用线性代数** | 安德鲁·唐加拉吉，印度理工学院马德拉斯 | [EE5120](http:\u002F\u002Fwww.ee.iitm.ac.in\u002F~andrew\u002FEE5120) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLyqSpQzTE6M-CHZU5RGfamcXOnuFyTOpm) | 2021 |\n| 23. | **数据科学的数学工具** | 卡洛斯·费尔南德斯-格兰达，纽约大学 | [DS-GA 1013\u002FMath-GA 2824](https:\u002F\u002Fcds.nyu.edu\u002Fmath-tools) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLBEf5mJtE6KtU6YlXFZD6lyYcHhW5pIlc) | 2021 |\n| 24. | **数值计算与机器学习的数学** | 瑞安·亚当斯，普林斯顿大学 | [COS 302 \u002F SML 305](https:\u002F\u002Fwww.cs.princeton.edu\u002Fcourses\u002Farchive\u002Fspring21\u002Fcos302) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLCO4cUaBLHFEHo42HVIVWaSOvbAiH30uc) | 2021 |\n| | | | | | |\n| | | | | | |\n\n[前往目录 :arrow_heading_up:](https:\u002F\u002Fgithub.com\u002Fkmario23\u002Fdeep-learning-drizzle#contents) \n\n:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::...（省略大量减号）::heavy_minus_sign:\n\n### :cupid: 机器学习优化 :cyclone: :boom: \n:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::...（省略大量减号）::heavy_minus_sign:\n\n| 序号 | 课程名称 | 大学\u002F讲师 | 课程网页 | 视频讲座 | 年份 |\n| ---- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | ---------- |\n| 1. | **凸优化** | 斯蒂芬·博伊德，斯坦福大学 | [ee364a](http:\u002F\u002Fweb.stanford.edu\u002Fclass\u002Fee364a\u002Flectures.html) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL3940DD956CDF0622) | 2008 |\n| 2. | **优化导论** | 迈克尔·齐布列夫斯基，以色列理工学院 | [CS-236330](https:\u002F\u002Fsites.google.com\u002Fsite\u002Fmichaelzibulevsky\u002Foptimization-course) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLDFB2EEF4DDAFE30B) | 2009 |\n| 3. | **机器学习中的优化** | S V N 维什瓦纳坦，普渡大学 | `无` | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL09B0E8AFC69BE108) | 2011 |\n| 4. | **优化** | 杰夫·戈登和瑞安·蒂布希拉尼，卡内基梅隆大学 | [10-725](https:\u002F\u002Fwww.cs.cmu.edu\u002F~ggordon\u002F10725-F12\u002F) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL7y-1rk2cCsDOv91McLOnV4kExFfTB7dU) | 2012 |\n| 5. | **凸优化** | 乔伊迪普·达塔，印度理工学院坎普尔分校 | [cvx-nptel](https:\u002F\u002Fnptel.ac.in\u002Fcourses\u002F111\u002F104\u002F111104068) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLbMVogVj5nJQHFqfiSdgaLCCWvDcm1W4l) | 2013 |\n| 6. | **优化基础** | 乔伊迪普·达塔，印度理工学院坎普尔分校 | [fop-nptel](https:\u002F\u002Fnptel.ac.in\u002Fcourses\u002F111\u002F104\u002F111104071) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLbMVogVj5nJRRbofh3Qm3P6_NVyevDGD_) | 2014 |\n| 7. | **机器学习的算法方面** | 安库尔·莫伊特拉，麻省理工学院 | [18.409-AAML](http:\u002F\u002Fpeople.csail.mit.edu\u002Fmoitra\u002F409.html) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLB3sDpSRdrOvI1hYXNsa6Lety7K8FhPpx) | 2015年春季学期 |\n| 8. | **数值优化** | 希里什·K·舍瓦德，印度科学研究所 | `无` | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL6EA0722B99332589) | 2015 |\n| 9. | **凸优化** | 瑞安·蒂布希拉尼，卡内基梅隆大学 | [10-725](https:\u002F\u002Fwww.stat.cmu.edu\u002F~ryantibs\u002Fconvexopt-S15\u002F) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLjbUi5mgii6BZBhJ9nW7eydgycyCOYeZ6) | 2015年春季学期 |\n| 10. | **凸优化** | 瑞安·蒂布希拉尼，卡内基梅隆大学 | [10-725](http:\u002F\u002Fstat.cmu.edu\u002F~ryantibs\u002Fconvexopt-F15\u002F) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLjbUi5mgii6AGJW3La3BpEXe27n8v3biT) | 2015年秋季学期 |\n| 11. | **高级算法** | 安库尔·莫伊特拉，麻省理工学院 | [6.854-AA](http:\u002F\u002Fpeople.csail.mit.edu\u002Fmoitra\u002F854.html) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL6ogFv-ieghdoGKGg2Bik3Gl1glBTEu8c) | 2016年春季学期 |\n| 12. | **优化导论** | 迈克尔·齐布列夫斯基，以色列理工学院 | `无` | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLBD31626529B0AC2A) | 2016 |\n| 13. | **凸优化** | 哈维尔·佩尼亚和瑞安·蒂布希拉尼 | [10-725\u002F36-725](https:\u002F\u002Fwww.stat.cmu.edu\u002F~ryantibs\u002Fconvexopt-F16) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLjbUi5mgii6AVdvImLB9-Hako68p9MpIC) | 2016年秋季学期 |\n| 14. | **凸优化** | 瑞安·蒂布希拉尼，卡内基梅隆大学 | [10-725](https:\u002F\u002Fwww.stat.cmu.edu\u002F~ryantibs\u002Fconvexopt-F18\u002F) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLpIxOj-HnDsMM7BCNGC3hPFU3DfCWfVIw) \u003Cbr\u002F> [讲座视频](https:\u002F\u002Fwww.stat.cmu.edu\u002F~ryantibs\u002Fconvexopt-F18\u002F) | 2018年秋季学期 |\n| 15. | **现代算法优化** | 尤里·涅斯特罗夫，鲁汶天主教大学 | `无` | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLEqoHzpnmTfAoUDqnmMly-KgyJ6ZM_axf) | 2018 |\n| 16. | **优化、优化基础** | 马克·沃克，亚利桑那大学 | [MathCamp-20](http:\u002F\u002Fwww.u.arizona.edu\u002F~mwalker\u002FMathCamp2020\u002FMathCamp2020LectureNotes.htm) | [YouTube-讲座-基础。](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLcjqUUQt__ZE6wp_c4-FcRdmzBvx8VN7O) \u003Cbr\u002F> [YouTube-讲座-优化](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLcjqUUQt__ZE0ZSTNRyBIgLJ5obPHdmxC) | 2019年至今 |\n| 17. | **优化：原理与算法** | 米歇尔·比尔莱尔，洛桑联邦理工学院 (EPFL) | [opt-algo](https:\u002F\u002Ftransp-or.epfl.ch\u002Fbooks\u002Foptimization\u002Fhtml\u002Fabout_book.html) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLM4Pv4KYYzGzOpWwsaV6GgllT6njsi1G-) | 2019 |\n| 18. | **优化与仿真** | 米歇尔·比尔莱尔，洛桑联邦理工学院 (EPFL) | [opt-sim](https:\u002F\u002Ftransp-or.epfl.ch\u002Fcourses\u002FOptSim2019\u002Fslides.php) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL10NOnsbP5Q5NlJ-Y6Eiup6RTSfkuj1TR) | 2019年春季学期 |\n| 19. | **巴西连续优化研讨会** | 许多业界大咖，里约热内卢纯粹与应用数学国家研究所 | [cont. opt.](https:\u002F\u002Fimpa.br\u002Feventos-do-impa\u002Feventos-2019\u002Fxiii-brazilian-workshop-on-continuous-optimization) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLo4jXE-LdDTQVZhnLPq2W31vJ1fq1VSp6) | 2019 |\n| 20. | **全球优化研讨会** | 许多业界大咖，维也纳大学 | [1W-OPT](https:\u002F\u002Fowos.univie.ac.at) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLBQo-yZOMzLWEcAptzTYOnwXo9hhXrAa2) | 2020年至今 |\n| | | | | | |\n| 21. | **凸优化II** | 康斯坦丁·卡拉马尼斯，德克萨斯大学奥斯汀分校 | [CVX-Optim-II](http:\u002F\u002Fusers.ece.utexas.edu\u002F~cmcaram\u002Fconstantine_caramanis\u002FAnnouncements.html) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLXsmhnDvpjORzPelSDs0LSDrfJcqyLlZc) | 2020年春季学期 |\n| 22. | **组合优化** | 康斯坦丁·卡拉马尼斯，德克萨斯大学奥斯汀分校 | [comb-op](https:\u002F\u002Fcaramanis.github.io\u002Fteaching\u002F) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLXsmhnDvpjORcTRFMVF3aUgyYlHsxfhNL) | 2020年秋季学期 |\n| 23. | **用于机器学习和工程的优化方法** | 尤利乌斯·普弗罗默、尤尔根·贝耶雷，卡尔斯鲁厄理工学院 (KIT) | [Optim-MLE](https:\u002F\u002Fies.iar.kit.edu\u002Flehre_1487.php), [幻灯片](https:\u002F\u002Fdrive.google.com\u002Fdrive\u002Ffolders\u002F1WWVWV4vDBIOkjZc6uFY3nfXvpaOUHcfb) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLdkTDauaUnQpzuOCZyUUZc0lxf4-PXNR5) | 2020–2021年冬季 |\n| | | | | | |\n\n[前往目录 :arrow_heading_up:](https:\u002F\u002Fgithub.com\u002Fkmario23\u002Fdeep-learning-drizzle#contents) \n\n:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::……（共64个减号）\n\n\n### :cupid: 通用机器学习 :cyclone: :boom: \n:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::……（共64个减号）\n\n| 序号 | 课程名称 | 大学\u002F讲师 | 课程网页 | 视频讲座 | 年份 |\n| ---- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | --------- |\n| 1. | **CS229: 机器学习** | 安德鲁·吴，斯坦福大学 | [CS229-old](https:\u002F\u002Fsee.stanford.edu\u002FCourse\u002FCS229\u002F) \u003Cbr\u002F> [CS229-new](http:\u002F\u002Fcs229.stanford.edu\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLA89DCFA6ADACE599) | 2007 |\n| 2. | **机器学习** | 杰弗里·米勒，布朗大学 | `mathematical monk` | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLD0F06AA0D2E8FFBA) | 2011 |\n| 3. | **机器学习** | 汤姆·米切尔，卡内基梅隆大学 | [10-701](http:\u002F\u002Fwww.cs.cmu.edu\u002F~tom\u002F10701_sp11\u002F) | [Lecture-Videos](http:\u002F\u002Fwww.cs.cmu.edu\u002F~tom\u002F10701_sp11\u002Flectures.shtml) | 2011 |\n| 4. | **机器学习与数据挖掘** | 南多·德·弗雷塔斯，不列颠哥伦比亚大学 | [CPSC-340](https:\u002F\u002Fwww.cs.ubc.ca\u002F~nando\u002F340-2012\u002Findex.php) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLE6Wd9FR--Ecf_5nCbnSQMHqORpiChfJf) | 2012 |\n| 5. | **从数据中学习** | 亚塞尔·阿布-穆斯塔法，加州理工学院 | [CS156](http:\u002F\u002Fwork.caltech.edu\u002Ftelecourse.html) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLD63A284B7615313A) | 2012 |\n| 6. | **机器学习** | 鲁道夫·特里贝尔，慕尼黑工业大学 | [Machine Learning](https:\u002F\u002Fvision.in.tum.de\u002Fteaching\u002Fws2013\u002Fml_ws13) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLTBdjV_4f-EIiongKlS9OKrBEp8QR47Wl) | 2013 |\n| 7. | **机器学习导论** | 亚历克斯·斯莫拉，卡内基梅隆大学 | [10-701](http:\u002F\u002Falex.smola.org\u002Fteaching\u002Fcmu2013-10-701\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLZSO_6-bSqHQmMKwWVvYwKreGu4b4kMU9) | 2013 |\n| 8. | **机器学习导论** | 亚历克斯·斯莫拉和杰弗里·戈登，卡内基梅隆大学 | [10-701x](http:\u002F\u002Falex.smola.org\u002Fteaching\u002Fcmu2013-10-701x\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLZSO_6-bSqHR7NPk4k0zqdm2dPdraQZ_B) | 2013 |\n| 9. | **模式识别** | 苏肯杜·达斯，印度理工学院马德拉斯分校，以及 C.A. 穆尔蒂，加尔各答统计研究所 | [PR-NPTEL](https:\u002F\u002Fnptel.ac.in\u002Fsyllabus\u002F106106046\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLbMVogVj5nJQJMLb2CYw9rry0d5s0TQRp) | 2014 |\n| 10. | **使用 R 语言的应用统计学习导论** | 特雷弗·哈斯蒂和罗伯特·蒂布希拉尼，斯坦福大学 | [stat-learn](https:\u002F\u002Flagunita.stanford.edu\u002Fcourses\u002FHumanitiesandScience\u002FStatLearning\u002FWinter2015\u002Fabout) \u003Cbr\u002F> [R-bloggers](https:\u002F\u002Fwww.r-bloggers.com\u002Fin-depth-introduction-to-machine-learning-in-15-hours-of-expert-videos\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLOg0ngHtcqbPTlZzRHA2ocQZqB1D_qZ5V) | 2014 |\n| | | | | | |\n| 11. | **机器学习导论** | 凯蒂·马洛尼、塞巴斯蒂安·瑟伦，优达学城 | [ML-Udacity](https:\u002F\u002Fwww.udacity.com\u002Fcourse\u002Fud120) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLAwxTw4SYaPkQXg8TkVdIvYv4HfLG7SiH) | 2015 |\n| 12. | **机器学习导论** | 德鲁夫·巴特拉，弗吉尼亚理工大学 | [ECE-5984](https:\u002F\u002Ffilebox.ece.vt.edu\u002F~s15ece5984\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL-fZD610i7yDUiNTFy-tEOxkTwg4mHZHu) | 2015 |\n| 13. | **统计学习——分类** | 阿里·戈德西，滑铁卢大学 | [STAT-441](https:\u002F\u002Fuwaterloo.ca\u002Fdata-analytics\u002Fstatistical-learning-classification) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLehuLRPyt1Hy-4ObWBK4Ab0xk97s6imfC) | 2015 |\n| 14. | **机器学习理论** | 沙伊·本-大卫，滑铁卢大学 | `无` | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLPW2keNyw-usgvmR7FTQ3ZRjfLs5jT4BO) | 2015 |\n| 15. | **机器学习导论** | 亚历克斯·斯莫拉，卡内基梅隆大学 | [10-701](http:\u002F\u002Falex.smola.org\u002Fteaching\u002F10-701-15\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLZSO_6-bSqHTTV7w9u7grTXBHMH-mw3qn) | S2015 |\n| 16. | **统计机器学习** | 莱瑞·瓦瑟曼，卡内基梅隆大学 | `无` | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLjbUi5mgii6BWEUZf7He6nowWvGne_Y8r) | S2015 |\n| 17. | **ML：监督学习** | 迈克尔·利特曼、查尔斯·伊斯贝尔、普什卡尔·科尔赫，佐治亚理工学院 | [ML-Udacity](https:\u002F\u002Feu.udacity.com\u002Fcourse\u002Fmachine-learning--ud262) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLAwxTw4SYaPl0N6-e1GvyLp5-MUMUjOKo) | 2015 |\n| 18. | **ML：无监督学习** | 迈克尔·利特曼、查尔斯·伊斯贝尔、普什卡尔·科尔赫，佐治亚理工学院 | [ML-Udacity](https:\u002F\u002Feu.udacity.com\u002Fcourse\u002Fmachine-learning-unsupervised-learning--ud741) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLAwxTw4SYaPmaHhu-Lz3mhLSj-YH-JnG7) | 2015 |\n| 19. | **高级机器学习导论** | 巴纳巴斯·波茨奥斯和亚历克斯·斯莫拉 | [10-715](https:\u002F\u002Fwww.cs.cmu.edu\u002F~bapoczos\u002FClasses\u002FML10715_2015Fall\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL4YhK0pT0ZhWBzSBkMGzpnPw6sf6Ma0IX) | F2015 |\n| 20. | **机器学习** | 佩德罗·多明戈斯，华盛顿大学 | [CSEP-546](https:\u002F\u002Fcourses.cs.washington.edu\u002Fcourses\u002Fcsep546\u002F16sp\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLTPQEx-31JXgtDaC6-3HxWcp7fq4N8YGr) | S2016 |\n| | | | | | |\n| 21. | **统计机器学习** | 莱瑞·瓦瑟曼，卡内基梅隆大学 | `无` | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLTB9VQq8WiaCBK2XrtYn5t9uuPdsNm7YE) | S2016 |\n| 22. | **大数据环境下的机器学习** | 威廉·科恩，卡内基梅隆大学 | [10-605](http:\u002F\u002Fcurtis.ml.cmu.edu\u002Fw\u002Fcourses\u002Findex.php\u002FMachine_Learning_with_Large_Datasets_10-605_in_Fall_2016) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLnfBqXRW5MRhPtfkadfwQ0VcuSi2IwEcW) | F2016 |\n| 23. | **机器学习的数学基础** | 杰弗里·戈登，卡内基梅隆大学 | `10-600` | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL7y-1rk2cCsA339crwXMWUaBRuLBvPBCg) | F2016 |\n| 24. | **统计学习——分类** | 阿里·戈德西，滑铁卢大学 | `无` | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLehuLRPyt1HzXDemu7K4ETcF0Ld_B5adG) | 2017 |\n| 25. | **机器学习** | 安德鲁·吴，斯坦福大学 | [Coursera-ML](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Fmachine-learning) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLLssT5z_DsK-h9vYZkQkYNWcItqhlRJLN) | 2017 |\n| 26. | **机器学习** | 罗尼·罗森菲尔德，卡内基梅隆大学 | [10-601](http:\u002F\u002Fwww.cs.cmu.edu\u002F~roni\u002F10601-f17\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL7k0r4t5c10-g7CWCnHfZOAxLaiNinChk) | 2017 |\n| 27. | **统计机器学习** | 瑞安·蒂布希拉尼、莱瑞·瓦瑟曼，卡内基梅隆大学 | [10-702](http:\u002F\u002Fwww.stat.cmu.edu\u002F~ryantibs\u002Fstatml\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLjbUi5mgii6B7A0nM74zHTOVQtTC9DaCv) | S2017 |\n| 28. | **用于计算机视觉的机器学习** | 弗雷德·汉普雷希特，海德堡大学 | `无` | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLuRaSnb3n4kSQFyt8VBldsQ9pO9Xtu8rY) | F2017 |\n| 29. | **机器学习的数学基础** | 杰弗里·戈登，卡内基梅隆大学 | [10-606 \u002F 10-607](https:\u002F\u002Fcanvas.cmu.edu\u002Fcourses\u002F603\u002Fassignments\u002Fsyllabus) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL7y-1rk2cCsAqRtWoZ95z-GMcecVG5mzA) | F2017 |\n| 30. | **数据可视化** | 阿里·戈德西，滑铁卢大学 | `无` | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLehuLRPyt1HzQoXEhtNuYTmd0aNQvtyAK) | 2017 |\n| | | | | | |\n| 31. | **面向物理学家的机器学习** | 弗洛里安·马夸特，埃尔兰根-纽伦堡大学 | [ML4Phy-17](http:\u002F\u002Fwww.thp2.nat.uni-erlangen.de\u002Findex.php\u002F2017_Machine_Learning_for_Physicists,_by_Florian_Marquardt) | [Lecture-Videos](https:\u002F\u002Fwww.video.uni-erlangen.de\u002Fcourse\u002Fid\u002F574) | 2017 |\n| 32. | **面向智能系统的机器学习** | 基利安·温贝格，康奈尔大学 | [CS4780](http:\u002F\u002Fwww.cs.cornell.edu\u002Fcourses\u002Fcs4780\u002F2018fa\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLl8OlHZGYOQ7bkVbuRthEsaLr7bONzbXS) | F2018 |\n| 33. | **统计学习理论及其应用** | 托马索·波乔、洛伦佐·罗萨斯科、萨莎·拉赫林 | [9.520\u002F6.860](https:\u002F\u002Fcbmm.mit.edu\u002Flh-9-520) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLyGKBDfnk-iAtLO6oLW4swMiQGz4f2OPY) | F2018 |\n| 34. | **机器学习与数据挖掘** | 迈克·盖尔巴特，不列颠哥伦比亚大学 | [CPSC-340](https:\u002F\u002Fubc-cs.github.io\u002Fcpsc340\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLWmXHcz_53Q02ZLeAxigki1JZFfCO6M-b) | 2018 |\n| 35. | **机器学习基础** | 大卫·罗森伯格，彭博社 | [FOML](https:\u002F\u002Fbloomberg.github.io\u002Ffoml\u002F#home) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLnZuxOufsXnvftwTB1HL6mel1V32w0ThI) | 2018 |\n| 36. | **机器学习导论** | 安德烈亚斯·克劳斯，苏黎世联邦理工学院 | [IntroML](https:\u002F\u002Flas.inf.ethz.ch\u002Fteaching\u002Fintroml-s18) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLzn6LN6WhlN273tsqyfdrBUsA-o5nUESV) | 2018 |\n| 37. | **机器学习基础** | 桑乔伊·达斯古普塔，加州大学圣地亚哥分校 | [MLF-slides](https:\u002F\u002Fdrive.google.com\u002Fdrive\u002Ffolders\u002F1l1rwv-jMihLZIpW0zTgGN9-snWOsA3M9) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL_onPhFCkVQhUzcTVgQiC8W2ShZKWlm0s) | 2018 |\n| 38. | **机器学习** | 乔丹·博伊德-格雷伯，马里兰大学 | [CMSC-726](http:\u002F\u002Fusers.umiacs.umd.edu\u002F~jbg\u002Fteaching\u002FCMSC_726\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLegWUnz91WfsELyRcZ7d1GwAVifDaZmgo) | 2015-2018 |\n| 39. | **机器学习** | 安德鲁·吴，斯坦福大学 | [CS229](http:\u002F\u002Fcs229.stanford.edu\u002Fsyllabus-autumn2018.html) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLoROMvodv4rMiGQp3WXShtMGgzqpfVfbU) | 2018 |\n| 40. | **机器智能** | H.R. 提佐什，滑铁卢大学 | [SYDE-522](https:\u002F\u002Fkimialab.uwaterloo.ca\u002Fkimia\u002Findex.php\u002Fteaching\u002Fsyde-522-machine-intelligence-2) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL4upCU5bnihwCX93Gv6AQnKmVMwx4AZoT) | 2019 |\n| | | | | | |\n| 41. | **机器学习导论** | 帕斯卡尔·普帕尔，滑铁卢大学 | [CS480\u002F680](https:\u002F\u002Fcs.uwaterloo.ca\u002F~ppoupart\u002Fteaching\u002Fcs480-spring19) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLdAoL1zKcqTW-uzoSVBNEecKHsnug_M0k) | S2019 |\n| 42. | **高级机器学习** | 托尔斯滕·约阿希姆斯，康奈尔大学 | [CS-6780](https:\u002F\u002Fwww.cs.cornell.edu\u002Fcourses\u002Fcs6780\u002F2019sp) | [Lecture-Videos](https:\u002F\u002Fcornell.mediasite.com\u002FMediasite\u002FCatalog\u002FFull\u002Ff5d1cd3323f746cca80b2468bf97efd421) | S2019 |\n| 43. | **面向结构化数据的机器学习** | 马特·戈姆利，卡内基梅隆大学 | [10-418\u002F10-618](http:\u002F\u002Fwww.cs.cmu.edu\u002F~mgormley\u002Fcourses\u002F10418\u002Fschedule.html) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL4CxkUJbvNVihRKP4bXufvRLIWzeS-ieP) | F2019 |\n| 44. | **高级机器学习** | 约阿希姆·布曼，苏黎世联邦理工学院 | [ML2-AML](https:\u002F\u002Fml2.inf.ethz.ch\u002Fcourses\u002Faml\u002F) | [Lecture-Videos](https:\u002F\u002Fvideo.ethz.ch\u002Flectures\u002Fd-infk\u002F2019\u002Fautumn\u002F252-0535-00L.html) | F2019 |\n| 45. | **面向信号处理的机器学习** | 维普尔·阿罗拉，印度理工学院坎普尔分校 | [MLSP](http:\u002F\u002Fhome.iitk.ac.in\u002F~vipular\u002Fstuff\u002F2019_MLSP.html) | [Lecture-Videos](https:\u002F\u002Fiitk-my.sharepoint.com\u002F:f:\u002Fg\u002Fpersonal\u002Fvipular_iitk_ac_in\u002FEnf97NZfsoVBiyclC6yHfe4BlUv6CA4U8LPQQ4vtsDo_Xg) | F2019 |\n| 46. | **机器学习基础** | 安娜玛什丽·阿南德库马尔，加州理工学院 | [CMS-165](http:\u002F\u002Ftensorlab.cms.caltech.edu\u002Fusers\u002Fanima\u002Fcms165-2019.html) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLVNifWxslHCA5GUh0o92neMiWiQiGVFqp) | 2019 |\n| 47. | **面向物理学家的机器学习** | 弗洛里安·马夸特，埃尔兰根-纽伦堡大学 | `无` | [Lecture-Videos](https:\u002F\u002Fwww.video.uni-erlangen.de\u002Fcourse\u002Fid\u002F778) | 2019 |\n| 48. | **应用机器学习** | 安德烈亚斯·穆勒，哥伦比亚大学 | [COMS-W4995](https:\u002F\u002Fwww.cs.columbia.edu\u002F~amueller\u002Fcomsw4995s19\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL_pVmAaAnxIQGzQS2oI3OWEPT-dpmwTfA) | 2019 |\n| 49. | **网络上的机器学习基础** | 霍赛因·肖克里-加迪科莱伊，瑞典皇家理工学院 | [MLoNs](https:\u002F\u002Fsites.google.com\u002Fview\u002Fmlons\u002Fcourse-materials) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLWoZTd81WFCEBFrxDfNUrDnt3ABdLfg80) | 2019 |\n| 50. | **机器学习与统计推断的基础** | 安娜玛什丽·阿南德库马尔，加州理工学院 | [CMS-165](http:\u002F\u002Ftensorlab.cms.caltech.edu\u002Fusers\u002Fanima\u002Fcms165-2020.html) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLVNifWxslHCDlbyitaLLYBOAEPbmF1AHg) | 2020 |\n| | | | | | |\n| 51. | **机器学习** | 丽贝卡·威莱特和于欣·陈，芝加哥大学 | [STAT 37710 \u002F CMSC 35400](https:\u002F\u002Fvoices.uchicago.edu\u002Fwillett\u002Fteaching\u002Fstats37710-cmsc35400-s20) | [Lecture-Videos](https:\u002F\u002Fvoices.uchicago.edu\u002Fwillett\u002Fteaching\u002Fstats37710-cmsc35400-s20) | S2020 |\n| 52. | **机器学习导论** | 桑杰·拉尔和斯蒂芬·博伊德，斯坦福大学 | [EE104\u002FCME107](http:\u002F\u002Fee104.stanford.edu) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLoROMvodv4rN_Uy7_wmS051_q1d6akXmK) | S2020 |\n| 53. | **应用机器学习** | 安德烈亚斯·穆勒，哥伦比亚大学 | [COMS-W4995](https:\u002F\u002Fwww.cs.columbia.edu\u002F~amueller\u002Fcomsw4995s20\u002Fschedule\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL_pVmAaAnxIRnSw6wiCpSvshFyCREZmlM) | S2020 |\n| 54. | **统计机器学习** | 乌尔丽克·冯·卢克斯堡，图宾根大学埃伯哈德·卡尔大学 | [Stat-ML](https:\u002F\u002Fwww.tml.cs.uni-tuebingen.de\u002Fteaching\u002F2020_statistical_learning\u002Findex.php) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL05umP7R6ij2XCvrRzLokX6EoHWaGA2cC) | SS2020 |\n| 55. | **概率机器学习** | 菲利普·亨尼格，图宾根大学埃伯哈德·卡尔大学 | [Prob-ML](https:\u002F\u002Funi-tuebingen.de\u002Fen\u002F180804) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL05umP7R6ij1tHaOFY96m5uX3J21a6yNd) | SS2020 |\n| 56. | **机器学习** | 萨拉特·钱达尔，PolyMTL、蒙特利尔大学、Mila | [INF8953CE](http:\u002F\u002Fsarathchandar.in\u002Fteaching\u002Fml\u002Ffall2020) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLImtCgowF_ET0mi-AmmqQ0SIJUpWYaIOr) | F2020 |\n| 57. | **机器学习** | 埃里克·贝克尔斯，阿姆斯特丹大学 | [UvA-ML](https:\u002F\u002Fuvaml1.github.io\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL8FnQMH2k7jzhtVYbKmvrMyXDYMmgjj_n) | F2020 |\n| 58. | **用于信号处理的神经网络** | 沙扬·斯里尼瓦萨·加拉尼，印度科学研究院 | [NN4SP](https:\u002F\u002Flabs.dese.iisc.ac.in\u002Fpnsil\u002Fneural-networks-and-learning-systems-i-fall-2020\u002F) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLgMDNELGJ1CZn1399dV7_U4VBNJflRsua) | F2020 |\n| 59. | **机器学习导论** | 德米特里·科巴克，图宾根大学医院 | `无` | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL05umP7R6ij35ShKLDqccJSDntugY4FQT) | 2020 |\n| 60. | **机器学习（PRML）** | 埃里克·J·贝克尔斯，阿姆斯特丹大学 | [UvAML-1](https:\u002F\u002Fuvaml1.github.io) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL8FnQMH2k7jzhtVYbKmvrMyXDYMmgjj_n) | 2020 |\n| | | | | | |\n| 61. | **使用核方法的机器学习** | 朱利安·迈拉尔和让-菲利普·韦尔特，Inria\u002F巴黎萨克莱高等师范学院、谷歌 | [ML-Kernels](http:\u002F\u002Fmembers.cbio.mines-paristech.fr\u002F~jvert\u002Fsvn\u002Fkernelcourse\u002Fcourse\u002F2021mva\u002Findex.html) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLD93kGj6_EdrkNj27AZMecbRlQ1SMkp_o) | S2021 |\n| 62. | **持续学习** | 文琴佐·洛莫纳科，比萨大学 | [ContLearn'21](https:\u002F\u002Fcourse.continualai.org\u002Fbackground\u002Fdetails) | [YouTube-Lectures](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLm6QXeaB-XkBfM5RgQP6wCR7Jegdg51Px) | 2021 |\n| 63. | **因果关系** | 克里斯蒂娜·海因策-德姆尔，苏黎世联邦理工学院 | [Causal'21](https:\u002F\u002Fstat.ethz.ch\u002Flectures\u002Fss21\u002Fcausality.php#course_materials) | [YouTube-Lectures](https:\u002F\u002Fstat.ethz.ch\u002Flectures\u002Fss21\u002Fcausality.php#course_materials) | 2021 |\n| | | | | | |\n\n[前往目录 :arrow_heading_up:](https:\u002F\u002Fgithub.com\u002Fkmario23\u002Fdeep-learning-drizzle#contents) \n\n:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::...（省略号表示重复）:heavy_minus_sign::heavy_minus_sign:\n\n### :balloon: 强化学习 :hotsprings: :video_game: \n:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::...（省略号表示重复）:heavy_minus_sign::heavy_minus_sign:\n\n| 序号 | 课程名称 | 大学\u002F讲师 | 课程网页 | 视频讲座 | 年份 |\n| ---- | -------------------------------------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | ------ |\n| 1. | **强化学习简明课程** | Satinder Singh, 密歇根大学 | `无` | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLM4Pv4KYYzGy4cIFQ5C36-1jMNLab80Ky) | 2011 |\n| 2. | **近似动态规划** | 迪米特里·P·伯特塞卡斯，麻省理工学院 | [讲义幻灯片](http:\u002F\u002Fadpthu2014.weebly.com\u002Fslides--materials.html) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLiCLbsFQNFAxOmVeqPhI5er1LGf2-L9I4) | 2014 |\n| 3. | **强化学习导论** | 戴维·西尔弗，DeepMind | [UCL-RL](http:\u002F\u002Fwww0.cs.ucl.ac.uk\u002Fstaff\u002Fd.silver\u002Fweb\u002FTeaching.html) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLqYmG7hTraZDM-OYHWgPebj2MfCFzFObQ) | 2015 |\n| 4. | **强化学习** | 查尔斯·伊斯贝尔、克里斯·普莱比，佐治亚理工学院；迈克尔·利特曼，布朗大学 | [RL-Udacity](https:\u002F\u002Feu.udacity.com\u002Fcourse\u002Freinforcement-learning--ud600) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLAwxTw4SYaPnidDwo9e2c7ixIsu_pdSNp) | 2015 |\n| 5. | **强化学习** | 巴拉拉曼·拉文德兰，印度理工学院马德拉斯 | [RL-IITM](https:\u002F\u002Fwww.cse.iitm.ac.in\u002F~ravi\u002Fcourses\u002FReinforcement%20Learning.html) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLNdWVHi37UggQIVcaZcmtGGEQHY9W7d9D) | 2016 |\n| 6. | **深度强化学习** | 谢尔盖·列文，加州大学伯克利分校 | [CS-294](http:\u002F\u002Frail.eecs.berkeley.edu\u002Fdeeprlcoursesp17\u002F) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLkFD6_40KJIwTmSbCv9OVJB3YaO4sFwkX) | 2017年春季 |\n| 7. | **深度强化学习** | 谢尔盖·列文，加州大学伯克利分校 | [CS-294](http:\u002F\u002Frail.eecs.berkeley.edu\u002Fdeeprlcourse-fa17\u002F) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLkFD6_40KJIznC9CDbVTjAF2oyt8_VAe3) | 2017年秋季 |\n| 8. | **深度RL训练营** | 许多业界大牛，加州大学伯克利分校 | [Deep-RL](https:\u002F\u002Fsites.google.com\u002Fview\u002Fdeep-rl-bootcamp\u002Flectures) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCTgM-VlXKuylPrZ_YGAJHOw\u002Fvideos) | 2017 |\n| 9 | **数据高效强化学习** | 众多业界大牛，加那利群岛 | [DERL-17](http:\u002F\u002Fdalimeeting.org\u002Fdali2017\u002Fdata-efficient-reinforcement-learning.html) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL-tWvTpyd1VAvDpxukup6w-SuZQQ7e8K8) | 2017 |\n| 10. | **深度强化学习** | 谢尔盖·列文，加州大学伯克利分校 | [CS-294-112](http:\u002F\u002Frail.eecs.berkeley.edu\u002Fdeeprlcourse-fa18\u002F) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLkFD6_40KJIxJMR-j5A1mkxK26gh_qg37) | 2018 |\n| | | | | | |\n| 11. | **强化学习** | 帕斯卡尔·普帕尔，滑铁卢大学 | [CS-885](https:\u002F\u002Fcs.uwaterloo.ca\u002F~ppoupart\u002Fteaching\u002Fcs885-spring18\u002F) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLdAoL1zKcqTXFJniO3Tqqn6xMBBL07EDc) | 2018 |\n| 12. | **深度强化学习与控制** | 卡特琳娜·弗拉吉亚达基和汤姆·米切尔，卡内基梅隆大学 | [10-703](http:\u002F\u002Fwww.andrew.cmu.edu\u002Fcourse\u002F10-703\u002F) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLpIxOj-HnDsNfvOwRKLsUobmnF2J1l5oV) | 2018 |\n| 13. | **强化学习与最优控制** | 迪米特里·伯特塞卡斯，亚利桑那州立大学 | [RLOC](http:\u002F\u002Fweb.mit.edu\u002Fdimitrib\u002Fwww\u002FRLbook.html) | [讲座视频](http:\u002F\u002Fweb.mit.edu\u002Fdimitrib\u002Fwww\u002FRLbook.html) | 2019 |\n| 14. | **强化学习** | 艾玛·布伦斯基尔，斯坦福大学 | [CS 234](http:\u002F\u002Fweb.stanford.edu\u002Fclass\u002Fcs234\u002Findex.html) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLoROMvodv4rOSOPzutgyCTapiGlY2Nd8u) | 2019 |\n| 15. | **强化学习日** | 众多业界大牛，微软研究院，纽约 | [RLD-19](https:\u002F\u002Fwww.microsoft.com\u002Fen-us\u002Fresearch\u002Fevent\u002Freinforcement-learning-day-2019\u002F#!agenda) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLD7HFcN7LXRe9nWEX3Up-RiCDi6-0mqVC) | 2019 |\n| 16. | **强化学习与控制的新方向** | 众多业界大牛，普林斯顿高等研究院 | [NDRLC-19](https:\u002F\u002Fwww.math.ias.edu\u002Fndrlc) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLdDZb3TwJPZ61sGqd6cbWCmTc275NrKu3) | 2019 |\n| 17. | **深度强化学习** | 谢尔盖·列文，加州大学伯克利分校 | [CS 285](http:\u002F\u002Frail.eecs.berkeley.edu\u002Fdeeprlcourse-fa19) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLkFD6_40KJIwhWJpGazJ9VSj9CFMkb79A) | 2019年秋季 |\n| 18. | **深度多任务与元学习** | 切尔西·芬恩，斯坦福大学 | [CS 330](https:\u002F\u002Fcs330.stanford.edu\u002F) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLoROMvodv4rMC6zfYmnD7UG3LVvwaITY5) | 2019年春季 |\n| 19. | **RL理论研讨会** | 众多业界大牛，全球各地 | [RL-theory-sem](https:\u002F\u002Fsites.google.com\u002Fview\u002Frltheoryseminars\u002Fpast-seminars) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCfBFutC9RbKK6p--B4R9ebA\u002Fvideos) | 2020年起 |\n| 20. | **深度强化学习** | 谢尔盖·列文，加州大学伯克利分校 | [CS 285](http:\u002F\u002Frail.eecs.berkeley.edu\u002Fdeeprlcourse-fa20) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL_iWQOsE6TfURIIhCrlt-wj9ByIVpbfGc) | 2020年秋季 |\n| | | | | | |\n| 21. | **强化学习导论** | 阿米尔-马苏德·法拉赫曼德，Vector研究所，多伦多大学 | [RL-intro](https:\u002F\u002Famfarahmand.github.io\u002FIntroRL) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLCveiXxL2xNbiDq51a8iJwPRq2aO0ykrq) | 2021年春季 |\n| 22. | **强化学习** | 安东尼奥·切拉尼和埃马努埃莱·帕尼宗，国际理论物理中心 | `无` | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLp0hSY2uBeP8q2G3mfHGVGvQFEMX0QRWM) | 2021 |\n| 23. | **计算感觉运动学习** | 普尔基特·阿格拉瓦尔，MIT-CSAIL | [6.884-CSL](https:\u002F\u002Fpulkitag.github.io\u002F6.884\u002Flectures) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLwNwxAG-kBxPMTIs2fKWSsf7HqL2TcC78) | 2021年春季 |\n| 24. | **强化学习** | 迪米特里·P·伯特塞卡斯，ASU\u002FMIT | [RL-21](http:\u002F\u002Fweb.mit.edu\u002Fdimitrib\u002Fwww\u002FRLbook.html) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLmH30BG15SIp79JRJ-MVF12uvB1qPtPzn) | 2021年春季 |\n| 25. | **强化学习** | 萨拉特·钱达尔，蒙特利尔理工大学 | [INF8953DE](https:\u002F\u002Fchandar-lab.github.io\u002FINF8953DE) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLImtCgowF_ES_JdF_UcM60EXTcGZg67Ua) | 2021年秋季 |\n| 26. | **深度强化学习** | 谢尔盖·列文，加州大学伯克利分校 | [CS 285](http:\u002F\u002Frail.eecs.berkeley.edu\u002Fdeeprlcourse) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL_iWQOsE6TfXxKgI1GgyV1B_Xa0DxE5eH) | 2021年秋季 |\n| 27. | **强化学习系列讲座** | 众多业界大牛，DeepMind与伦敦大学共同举办 | [RL-series](https:\u002F\u002Fdeepmind.com\u002Flearning-resources\u002Freinforcement-learning-series-2021) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLqYmG7hTraZDVH599EItlEWsUOsJbAodm) | 2021 |\n| 28. | **强化学习** | 迪米特里·P·伯特塞卡斯，ASU\u002FMIT | [RL-22](http:\u002F\u002Fweb.mit.edu\u002Fdimitrib\u002Fwww\u002FRLbook.html) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLmH30BG15SIoXhxLldoio0BhsIY84YMDj) | 2022年春季 |\n| | | | | | |\n\n[前往目录 :arrow_heading_up:](https:\u002F\u002Fgithub.com\u002Fkmario23\u002Fdeep-learning-drizzle#contents) \n\n:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:\n\n### :loudspeaker: 概率图模型 :sparkles: \n:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:\n\n| 序号 | 课程名称 | 大学\u002F讲师 | 课程网页 | 讲座视频 | 年份 |\n| ---- | ------------------------------------------------------------ | --------------------------------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------- |\n| 1. | **概率图模型** | 众多名家，MPI-IS | [MLSS-图宾根](http:\u002F\u002Fmlss.tuebingen.mpg.de\u002F2013\u002F2013\u002Fspeakers.html) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLL0GjJzXhAWTRiW_ynFswMaiLSa0hjCZ3) | 2013 |\n| 2. | **概率建模与机器学习** | 祖宾·加拉马尼，剑桥大学 | [WUST-弗罗茨瓦夫](https:\u002F\u002Fwww.ii.pwr.edu.pl\u002F~gonczarek\u002Fzoubin.html) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLwUOK5j_XOsdfVAGKErx9HqnrVZIuRbZ2) | 2013 |\n| 3. | **概率图模型** | 埃里克·辛格，卡内基梅隆大学 | [10-708](http:\u002F\u002Fwww.cs.cmu.edu\u002F~epxing\u002FClass\u002F10708\u002Flecture.html) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLI3nIOD-p5aoXrOzTd1P6CcLavu9rNtC-) | 2014 |\n| 4. | **结构化数据学习：概率图模型导论** | 克里斯托夫·兰佩特，奥地利科学与技术研究所 | `无` | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLEqoHzpnmTfA0wc1JxjoVVOrJlx8W0rGf) | 2016 |\n| 5. | **概率图模型** | 尼古拉斯·扎巴拉什，圣母大学 | [PGM](https:\u002F\u002Fwww.zabaras.com\u002Fprobabilistic-graphical-models) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLd-PuDzW85AcV4bgdu7wHPL37hm60W4RM) | 2018 |\n| 6. | **概率图模型** | 埃里克·辛格，卡内基梅隆大学 | [10-708](https:\u002F\u002Fsailinglab.github.io\u002Fpgm-spring-2019\u002F) | [讲座视频](https:\u002F\u002Fsailinglab.github.io\u002Fpgm-spring-2019\u002Flectures) \u003Cbr> [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLoZgVqqHOumTY2CAQHL45tQp6kmDnDcqn) | S2019 |\n| 7. | **概率图模型** | 埃里克·辛格，卡内基梅隆大学 | [10-708](https:\u002F\u002Fwww.cs.cmu.edu\u002F~epxing\u002FClass\u002F10708-20\u002Findex.html) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLoZgVqqHOumTqxIhcdcpOAJOOimrRCGZn) | S2020 |\n| 8. | **人工智能中的不确定性建模** | 吉姆·希·李，新加坡国立大学 (NUS) | [CS 5340 - CH](https:\u002F\u002Fwww.coursehero.com\u002Fsitemap\u002Fschools\u002F2652-National-University-of-Singapore\u002Fcourses\u002F7821096-CS5340\u002F), [CS 5340-NB](https:\u002F\u002Fgithub.com\u002Fclear-nus\u002FCS5340-notebooks) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLxg0CGqViygOb9Eyc8IXM27doxjp2SK0H) | 2020-21 |\n| | | | | | |\n\n[前往目录 :arrow_heading_up:](https:\u002F\u002Fgithub.com\u002Fkmario23\u002Fdeep-learning-drizzle#contents)\n\n:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:\n\n## :game_die: 贝叶斯深度学习 :spades: :gem: \n:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:\n\n| 序号 | 课程名称 | 大学\u002F讲师 | 课程网页 | 讲座视频 | 年份 |\n| ---- | --------------------------------------------------- | --------------------------------- | -------------------------------------------------------- | ------------------------------------------------------------ | -------- |\n| 1. | **贝叶斯神经网络，变分推断** | 许多大牛 | `无` | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLM4Pv4KYYzGwUB4bFy183hwGhpL9ytvA1) | 2014至今 |\n| 2. | **变分推断** | 吴志杰，东北大学 | `无` | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLdk2fd27CQzSd1sQ3kBYL4vtv6GjXvPsE) | 2015 |\n| 3. | **深度学习与贝叶斯方法** | 许多大牛，莫斯科高等经济学院 | [DLBM-SS](http:\u002F\u002Fdeepbayes.ru\u002F2018) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLe5rNUydzV9Q01vWCP9BV7NhJG3j7mz62) | 2018 |\n| 4. | **深度学习与贝叶斯方法** | 许多大牛，莫斯科高等经济学院 | [DLBM-SS](http:\u002F\u002Fdeepbayes.ru\u002F) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLe5rNUydzV9QHe8VDStpU0o8Yp63OecdW) | 2019 |\n| 5. | **北欧概率人工智能** | 许多大牛，挪威科技大学，特隆赫姆 | [ProbAI](https:\u002F\u002Fgithub.com\u002Fprobabilisticai\u002Fprobai-2019) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLRy-VW__9hV8s--JkHXZvnd26KgjRP2ik) | 2019 |\n| | | | | | |\n\n[返回目录 :arrow_heading_up:](https:\u002F\u002Fgithub.com\u002Fkmario23\u002Fdeep-learning-drizzle#contents)\n\n:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:\n\n## :movie_camera: 医学成像 :camera: :video_camera: \n:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:\n\n| 序号 | 课程名称 | 大学\u002F讲师 | 课程网页 | 讲座视频 | 年份 |\n| ---- | ------------------------------------------------------------ | ------------------------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ----- |\n| 1. | **医学成像暑期学校** | 众多名家，西西里岛 | [MISS-14](http:\u002F\u002Fiplab.dmi.unict.it\u002Fmiss14\u002Fprogramme.html) | [YouTube讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL_VeUGLULXQtvcCdAgmvKoJ1k0Ajhz-Qu) | 2014 |\n| 2. | **生物医学图像分析暑期学校** | 众多名家，巴黎 | `无` | [YouTube讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLgSHH6boFf5uJAUT4ZRiAZc_ofXolkAGK) | 2015 |\n| 3. | **医学成像暑期学校** | 众多名家，西西里岛 | [MISS-16](http:\u002F\u002Fiplab.dmi.unict.it\u002Fmiss16\u002Fprogramme.html) | [YouTube讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLTRCr47yTx5iXIYSneX3LKf16upaw59wa) | 2016 |\n| 4. | **光学与超声成像 - OPUS** | 众多名家，法国里昂大学 | [OPUS'16](https:\u002F\u002Fopus2016lyon.sciencesconf.org\u002Fresource\u002Fpage\u002Fid\u002F2) | [YouTube讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL95ayoVLX8GdUKbxu-R9WqRWwzdWcKjti) | 2016 |\n| 5. | **医学成像暑期学校** | 众多名家，西西里岛 | [MISS-18](http:\u002F\u002Fiplab.dmi.unict.it\u002Fmiss\u002Fprogramme.htm) | [YouTube讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL_VeUGLULXQux1dV4iA3XuMX6AueJmGGa) | 2018 |\n| 6. | **医疗健康领域的人工智能研讨会** | 众多名家，斯坦福大学 | [CS 522](http:\u002F\u002Fcs522.stanford.edu\u002F2018\u002Findex.html) | [YouTube讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLYn-ZmPR1DtNQJ-ot-L2V2EgUEH6OH_7w) | 2018 |\n| 7. | **面向医疗健康的机器学习** | David Sontag, Peter Szolovits, CSAIL MIT | [MLHC-19](https:\u002F\u002Fmlhc19mit.github.io\u002F) \u003Cbr\u002F>[MIT 6.S897](https:\u002F\u002Focw.mit.edu\u002Fcourses\u002Felectrical-engineering-and-computer-science\u002F6-s897-machine-learning-for-healthcare-spring-2019\u002Flecture-notes\u002F) | [YouTube讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLUl4u3cNGP60B0PQXVQyGNdCyCTDU1Q5j) | 2019年春季学期 |\n| 8. | **深度学习及其在医学中的应用** | 众多名家，IPAM，加州大学洛杉矶分校 | [DLM-20](https:\u002F\u002Fwww.ipam.ucla.edu\u002Fprograms\u002Fworkshops\u002Fdeep-learning-and-medical-applications\u002F?tab=schedule) | [讲座视频](https:\u002F\u002Fwww.ipam.ucla.edu\u002Fprograms\u002Fworkshops\u002Fdeep-learning-and-medical-applications\u002F?tab=schedule) | 2020 |\n| 9. | **斯坦福医学与影像领域人工智能研讨会** | 众多名家，斯坦福AIMI | [AIMI-20](https:\u002F\u002Faimi.stanford.edu\u002Fnews-events\u002Faimi-symposium\u002Fagenda) | [YouTube讲座](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=tR2ObiL4il8&list=PLe6zdIMe5B7IR0oDOobXBDBlYY1eqLYPx) | 2020 |\n| | | | | | |\n\n[返回目录 :arrow_heading_up:](https:\u002F\u002Fgithub.com\u002Fkmario23\u002Fdeep-learning-drizzle#contents)\n\n:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:\n## :tada: 图神经网络（几何深度学习） :confetti_ball: :balloon: \n:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:\n\n| 序号 | 课程名称 | 大学\u002F讲师 | 课程网页 | 讲座视频 | 年份 |\n| ---- | ------------------------------------------------------------ | ------------------------------------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ----- |\n| 1. | **图与流形上的深度学习** | 迈克尔·布朗斯坦，以色列理工学院 | `无` | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLH39kM3nuavcVOUIIBraBNHjv-CwEd1uV) | 2017 |\n| 2. | **图与流形上的几何深度学习** | 迈克尔·布朗斯坦，慕尼黑工业大学 | `无` | [讲座-第一部分](https:\u002F\u002Fstreams.tum.de\u002FMediasite\u002FPlay\u002F1f3b894e78f6400daa7885c886b936fb1d), \u003Cbr\u002F>[讲座-第二部分](https:\u002F\u002Fstreams.tum.de\u002FMediasite\u002FPlay\u002F6039c846b2f84e7a806024c06e3f5c5c1d) | 2017 |\n| 3. | **欧洲图形学几何处理研讨会 - 研究生课程** | 众多大师，SIGGRAPH，伦敦 | [SGP-2017](http:\u002F\u002Fgeometry.cs.ucl.ac.uk\u002FSGP2017\u002F?p=gradschool) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLOp-ngXvomHArqntgLVNzuJNdzNx3rDjZ) | 2017 |\n| 4. | **欧洲图形学几何处理研讨会 - 研究生课程** | 众多大师，SIGGRAPH，巴黎 | [SGP-2018](https:\u002F\u002Fsgp2018.sciencesconf.org\u002Fresource\u002Fpage\u002Fid\u002F7) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLvcoRb-DvAmgpp8LYw7dUvLxh-1Vrrm-v) | 2018 |\n| 5. | **网络分析：基于图的挖掘与学习** | 尤雷·莱斯科韦茨，斯坦福大学 | [CS224W](http:\u002F\u002Fsnap.stanford.edu\u002Fclass\u002Fcs224w-2018\u002F) | [讲座视频](http:\u002F\u002Fsnap.stanford.edu\u002Fclass\u002Fcs224w-2018\u002F) | 2018 |\n| 6. | **基于图的机器学习** | 尤雷·莱斯科韦茨，斯坦福大学 | [CS224W](http:\u002F\u002Fsnap.stanford.edu\u002Fclass\u002Fcs224w-2019\u002F) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL-Y8zK4dwCrQyASidb2mjj_itW2-YYx6-) | 2019 |\n| 7. | 三维及更高维度中的几何与数据学习 - **几何与数据学习教程** | 众多大师，加州大学洛杉矶分校IPAM | [GLDT](http:\u002F\u002Fwww.ipam.ucla.edu\u002Fprograms\u002Fworkshops\u002Fgeometry-and-learning-from-data-tutorials) | [讲座视频](http:\u002F\u002Fwww.ipam.ucla.edu\u002Fprograms\u002Fworkshops\u002Fgeometry-and-learning-from-data-tutorials\u002F?tab=schedule) | 2019 |\n| 8. | 三维及更高维度中的几何与数据学习 - **几何处理** | 众多大师，加州大学洛杉矶分校IPAM | [GeoPro](http:\u002F\u002Fwww.ipam.ucla.edu\u002Fprograms\u002Fworkshops\u002Fworkshop-i-geometric-processing\u002F) | [讲座视频](http:\u002F\u002Fwww.ipam.ucla.edu\u002Fprograms\u002Fworkshops\u002Fworkshop-i-geometric-processing\u002F?tab=schedule) | 2019 |\n| 9. | 三维及更高维度中的几何与数据学习 - **形状分析** | 众多大师，加州大学洛杉矶分校IPAM | [Shape-Analysis](http:\u002F\u002Fwww.ipam.ucla.edu\u002Fprograms\u002Fworkshops\u002Fworkshop-ii-shape-analysis\u002F) | [讲座视频](http:\u002F\u002Fwww.ipam.ucla.edu\u002Fprograms\u002Fworkshops\u002Fworkshop-ii-shape-analysis\u002F?tab=schedule) | 2019 |\n| 10. | 三维及更高维度中的几何与数据学习 - **大数据的几何** | 众多大师，加州大学洛杉矶分校IPAM | [Geo-BData](http:\u002F\u002Fwww.ipam.ucla.edu\u002Fprograms\u002Fworkshops\u002Fworkshop-iii-geometry-of-big-data) | [讲座视频](http:\u002F\u002Fwww.ipam.ucla.edu\u002Fprograms\u002Fworkshops\u002Fworkshop-iii-geometry-of-big-data\u002F?tab=schedule) | 2019 |\n| | | | | | |\n| 11. | 三维及更高维度中的几何与数据学习 - **大数据的深度几何学习及其应用** | 众多大师，加州大学洛杉矶分校IPAM | [DGL-BData](http:\u002F\u002Fwww.ipam.ucla.edu\u002Fprograms\u002Fworkshops\u002Fworkshop-iv-deep-geometric-learning-of-big-data-and-applications) | [讲座视频](http:\u002F\u002Fwww.ipam.ucla.edu\u002Fprograms\u002Fworkshops\u002Fworkshop-iv-deep-geometric-learning-of-big-data-and-applications\u002F?tab=schedule) | 2019 |\n| 12. | **以色列几何深度学习** | 众多大师，以色列 | [iGDL-20](https:\u002F\u002Fgdl-israel.github.io\u002Fschedule.html) | [讲座视频](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=c8_32IVn-sg) | 2020 |\n| 13. | **面向图和序列数据的机器学习** | 施特凡·居内曼，慕尼黑工业大学 (TUM) | [MLGS-20](https:\u002F\u002Fwww.in.tum.de\u002Fen\u002Fdaml\u002Fteaching\u002Fsummer-term-2020\u002Fmachine-learning-for-graphs-and-sequential-data\u002F) | [讲座视频](https:\u002F\u002Fwww.in.tum.de\u002Fdaml\u002Fteaching\u002Fmlgs\u002F) | S2020 |\n| 14. | **基于图的机器学习** | 尤雷·莱斯科韦茨，斯坦福 | [CS224W](http:\u002F\u002Fweb.stanford.edu\u002Fclass\u002Fcs224w) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLoROMvodv4rPLKxIpqhjhPgdQy7imNkDn) | W2021 |\n| 15. | **几何深度学习** - AMMI | 众多大师，线上 | [GDL-AMMI](https:\u002F\u002Fgeometricdeeplearning.com\u002Flectures) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLn2-dEmQeTfQ8YVuHBOvAhUlnIPYxkeu3) | 2021 |\n| 16. | **几何深度学习暑期学校** - | 众多大师，丹麦技术大学、哥本哈根大学和奥胡斯大学 | [GDL- DTU, DIKU & AAU](https:\u002F\u002Fgeometric-deep-learning.compute.dtu.dk) | [讲座视频](https:\u002F\u002Fgeometric-deep-learning.compute.dtu.dk\u002Ftalks-and-materials) | 2021 |\n| 17. | **图神经网络** | 亚历杭德罗·里贝罗，宾夕法尼亚大学 | [ESE 514](https:\u002F\u002Fgnn.seas.upenn.edu) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUC_YPrqpiEqkeGOG1TCt0giQ\u002Fplaylists) | F2021 |\n| | | | | | |\n\n[返回目录 :arrow_heading_up:](https:\u002F\u002Fgithub.com\u002Fkmario23\u002Fdeep-learning-drizzle#contents)\n\n:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:\n\n\n### :hibiscus: 自然语言处理 :cherry_blossom: :sparkling_heart: \n:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:\n\n| 序号 | 课程名称 | 大学\u002F讲师 | 课程网页 | 讲座视频 | 年份 |\n| ---- | --------------------------------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | --------- |\n| 1. | **计算语言学I** | 约旦·博伊德-格雷伯，马里兰大学 | [CMS-723](http:\u002F\u002Fusers.umiacs.umd.edu\u002F~jbg\u002Fteaching\u002FCMSC_723\u002F) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLegWUnz91WfuPebLI97-WueAP90JO-15i) | 2013-2018 |\n| 2. | **自然语言处理的深度学习** | 尼尔斯·赖默斯，达姆施塔特工业大学 | [DL4NLP](https:\u002F\u002Fgithub.com\u002FUKPLab\u002Fdeeplearning4nlp-tutorial) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUC1zCuTrfpjT6Sv2kJk-JkvA\u002Fvideos) | 2015-2017 |\n| 3. | **自然语言处理的深度学习** | 众多名师，DeepMind-牛津 | [DL-NLP](http:\u002F\u002Fwww.cs.ox.ac.uk\u002Fteaching\u002Fcourses\u002F2016-2017\u002Fdl\u002F) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL613dYIGMXoZBtZhbyiBqb0QtgK6oJbpm) | 2017 |\n| 4. | **语音与语言的深度学习** | 巴塞罗那理工大学 | [DL-SL](https:\u002F\u002Ftelecombcn-dl.github.io\u002F2017-dlsl\u002F) | [讲座视频](https:\u002F\u002Ftelecombcn-dl.github.io\u002F2017-dlsl\u002F) | 2017 |\n| 5. | **自然语言处理的神经网络** | 格雷厄姆·诺伊比格，卡内基梅隆大学 | [NN4NLP](http:\u002F\u002Fwww.phontron.com\u002Fclass\u002Fnn4nlp2017\u002F) [代码](https:\u002F\u002Fgithub.com\u002Fneubig\u002Fnn4nlp-code) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL8PYTP1V4I8ABXzdqtOpB_eqBlVAz_xPT) | 2017 |\n| 6. | **自然语言处理的神经网络** | 格雷厄姆·诺伊比格，卡内基梅隆大学 | [NN4-NLP](http:\u002F\u002Fwww.phontron.com\u002Fclass\u002Fnn4nlp2018\u002F) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL8PYTP1V4I8Ba7-rY4FoB4-jfuJ7VDKEE) | 2018 |\n| 7. | **NLP的深度学习** | 马英年，新加坡国立大学 | [CS-6101](https:\u002F\u002Fwww.comp.nus.edu.sg\u002F~kanmy\u002Fcourses\u002F6101_1810\u002F) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLllwxvcS7ca5eD44KTCiT7Rmu_hFAafXB) | 2018 |\n| 8. | **自然语言处理的神经网络** | 格雷厄姆·诺伊比格，卡内基梅隆大学 | [NN4NLP](http:\u002F\u002Fwww.phontron.com\u002Fclass\u002Fnn4nlp2019\u002F) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL8PYTP1V4I8Ajj7sY6sdtmjgkt7eo2VMs) | 2019 |\n| 9. | **深度学习驱动的自然语言处理** | 阿比盖尔·西、克里斯·曼宁、理查德·索彻，斯坦福大学 | [CS224n](http:\u002F\u002Fweb.stanford.edu\u002Fclass\u002Fcs224n\u002F) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLoROMvodv4rOhcuXMZkNm7j3fVwBBY42z) | 2019 |\n| 10. | **自然语言理解** | 比尔·麦卡特尼和克里斯托弗·波茨 | [CS224U](https:\u002F\u002Fweb.stanford.edu\u002Fclass\u002Fcs224u) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLoROMvodv4rObpMCir6rNNUlFAn56Js20) | S2019 |\n| | | | | | |\n| 11. | **自然语言处理的神经网络** | 格雷厄姆·诺伊比格，卡内基梅隆大学 | [CS 11-747](http:\u002F\u002Fwww.phontron.com\u002Fclass\u002Fnn4nlp2020\u002Fschedule.html) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL8PYTP1V4I8CJ7nMxMC8aXv8WqKYwj-aJ) | S2020 |\n| 12. | **高级自然语言处理** | 莫希特·艾耶尔，马萨诸塞大学阿默斯特分校 | [CS 685](https:\u002F\u002Fpeople.cs.umass.edu\u002F~miyyer\u002Fcs685) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLWnsVgP6CzadmQX6qevbar3_vDBioWHJL) | F2020 |\n| 13. | **机器翻译** | 菲利普·科恩，约翰斯·霍普金斯大学 | [EN 601.468\u002F668](http:\u002F\u002Fmt-class.org\u002Fjhu\u002Fsyllabus.html) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLQrCiUDqDLG0lQX54o9jB4phJ-SLI6ZBQ) | F2020 |\n| 14. | **NLP的神经网络** | 格雷厄姆·诺伊比格，卡内基梅隆大学 | [CS 11-747](http:\u002F\u002Fwww.phontron.com\u002Fclass\u002Fnn4nlp2021) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL8PYTP1V4I8AkaHEJ7lOOrlex-pcxS-XV) | 2021 |\n| 15. | **自然语言处理的深度学习** | 崔京贤，纽约大学 | [DS-GA 1011](https:\u002F\u002Fdrive.google.com\u002Fdrive\u002Ffolders\u002F1ykXBtophaY_65VHK_8yDzZQJwfJDD5Ve) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLdH9u0f1XKW_s-c8EcgJpn_HJz5Jj1IRf) | F2021 |\n| 16. | **深度学习驱动的自然语言处理** | 克里斯·曼宁，斯坦福大学 | [CS224n](https:\u002F\u002Fweb.stanford.edu\u002Fclass\u002Farchive\u002Fcs\u002Fcs224n\u002Fcs224n.1214\u002F) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLoROMvodv4rOSH4v6133s9LFPRHjEmbmJ) | 2021 |\n| | | | | | |\n\n[返回目录 :arrow_heading_up:](https:\u002F\u002Fgithub.com\u002Fkmario23\u002Fdeep-learning-drizzle#contents) \n\n:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:\n\n### :speaking_head: 自动语音识别 :speech_balloon: :thought_balloon:\n| 序号 | 课程名称 | 大学\u002F讲师 | 课程网页 | 讲座视频 | 年份 |\n| ---- | ---------------------------------------- | ------------------------------ | --------------------------------------------------- | ------------------------------------------------------------ | --------- |\n| 1. | **语音与语言的深度学习** | 巴塞罗那理工大学 | [DL-SL](https:\u002F\u002Ftelecombcn-dl.github.io\u002F2017-dlsl\u002F) | [讲座视频](https:\u002F\u002Ftelecombcn-dl.github.io\u002F2017-dlsl\u002F) \u003Cbr\u002F> [YouTube视频](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL-5DCZHuHZkWeF9ljIjoC_X5gHRLNtIkU) | 2017 |\n| 2. | **东北地区的语音与音频** | 许多传奇人物，谷歌纽约 | [SANE-15](http:\u002F\u002Fwww.saneworkshop.org\u002Fsane2015\u002F) | [YouTube视频](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLBJWRPcgwk7sZOB4UTVilWWnRg84L9o5i) | 2015 |\n| 3. | **自动语音识别** | Samudra Vijaya K, TIFR | `无` | [YouTube视频](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCHk6uq1Cr9J3k5KNmIsYUNw\u002Fvideos) | 2016 |\n| 4. | **东北地区的语音与音频** | 许多传奇人物，谷歌纽约 | [SANE-17](http:\u002F\u002Fwww.saneworkshop.org\u002Fsane2017\u002F) | [YouTube视频](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLBJWRPcgwk7tNLaBVu_S90ZQSblO3bwjg) | 2017 |\n| 5. | **东北地区的语音与音频** | 许多传奇人物，谷歌剑桥 | [SANE-18](http:\u002F\u002Fwww.saneworkshop.org\u002Fsane2018\u002F) | [YouTube视频](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLBJWRPcgwk7sjMANn8jqosyHIMe6DJhmn) | 2018 |\n| | | | | | |\n| -1. | **用于语音识别的深度学习** | 许多传奇人物，AoE | `无` | [YouTube视频](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLM4Pv4KYYzGyFYCXV6YPWAKVOR2gmHnQd) | 2015-2018 |\n\n[返回目录 :arrow_heading_up:](https:\u002F\u002Fgithub.com\u002Fkmario23\u002Fdeep-learning-drizzle#contents) \n\n\n| 序号 | 课程名称 | 大学\u002F讲师 | 课程网页 | 讲座视频 | 年份 |\n| ---- | ---------------------------------------- | ------------------------------ | --------------------------------------------------- | ------------------------------------------------------------ | --------- |\n| 1. | **语音与语言的深度学习** | 巴塞罗那理工大学 | [DL-SL](https:\u002F\u002Ftelecombcn-dl.github.io\u002F2017-dlsl\u002F) | [讲座视频](https:\u002F\u002Ftelecombcn-dl.github.io\u002F2017-dlsl\u002F) \u003Cbr\u002F> [YouTube视频](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL-5DCZHuHZkWeF9ljIjoC_X5gHRLNtIkU) | 2017 |\n| 2. | **东北地区的语音与音频** | 许多传奇人物，谷歌纽约 | [SANE-15](http:\u002F\u002Fwww.saneworkshop.org\u002Fsane2015\u002F) | [YouTube视频](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLBJWRPcgwk7sZOB4UTVilWWnRg84L9o5i) | 2015 |\n| 3. | **自动语音识别** | Samudra Vijaya K, TIFR | `无` | [YouTube视频](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCHk6uq1Cr9J3k5KNmIsYUNw\u002Fvideos) | 2016 |\n| 4. | **东北地区的语音与音频** | 许多传奇人物，谷歌纽约 | [SANE-17](http:\u002F\u002Fwww.saneworkshop.org\u002Fsane2017\u002F) | [YouTube视频](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLBJWRPcgwk7tNLaBVu_S90ZQSblO3bwjg) | 2017 |\n| 5. | **东北地区的语音与音频** | 许多传奇人物，谷歌剑桥 | [SANE-18](http:\u002F\u002Fwww.saneworkshop.org\u002Fsane2018\u002F) | [YouTube视频](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLBJWRPcgwk7sjMANn8jqosyHIMe6DJhmn) | 2018 |\n| | | | | | |\n| -1. | **用于语音识别的深度学习** | 许多传奇人物，AoE | `无` | [YouTube视频](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLM4Pv4KYYzGyFYCXV6YPWAKVOR2gmHnQd) | 2015-2018 |\n\n[返回目录 :arrow_heading_up:](https:\u002F\u002Fgithub.com\u002Fkmario23\u002Fdeep-learning-drizzle#contents) \n\n:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign......### :speaking_head: 自动语音识别 :speech_balloon: :thought_balloon:\n| 序号 | 课程名称 | 大学\u002F讲师 | 课程网页 | 讲座视频 | 年份 |\n| ---- | ---------------------------------------- | ------------------------------ | --------------------------------------------------- | ------------------------------------------------------------ | --------- |\n| 1. | **语音与语言的深度学习** | 巴塞罗那理工大学 | [DL-SL](https:\u002F\u002Ftelecombcn-dl.github.io\u002F2017-dlsl\u002F) | [讲座视频](https:\u002F\u002Ftelecombcn-dl.github.io\u002F2017-dlsl\u002F) \u003Cbr\u002F> [YouTube视频](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL-5DCZHuHZkWeF9ljIjoC_X5gHRLNtIkU) | 2017 |\n| 2. | **东北地区的语音与音频** | 许多传奇人物，谷歌纽约 | [SANE-15](http:\u002F\u002Fwww.saneworkshop.org\u002Fsane2015\u002F) | [YouTube视频](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLBJWRPcgwk7sZOB4UTVilWWnRg84L9o5i) | 2015 |\n| 3. | **自动语音识别** | Samudra Vijaya K, TIFR | `无` | [YouTube视频](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCHk6uq1Cr9J3k5KNmIsYUNw\u002Fvideos) | 2016 |\n| 4. | **东北地区的语音与音频** | 许多传奇人物，谷歌纽约 | [SANE-17](http:\u002F\u002Fwww.saneworkshop.org\u002Fsane2017\u002F) | [YouTube视频](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLBJWRPcgwk7tNLaBVu_S90ZQSblO3bwjg) | 2017 |\n| 5. | **东北地区的语音与音频** | 许多传奇人物，谷歌剑桥 | [SANE-18](http:\u002F\u002Fwww.saneworkshop.org\u002Fsane2018\u002F) | [YouTube视频](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLBJWRPcgwk7sjMANn8jqosyHIMe6DJhmn) | 2018 |\n\n[返回目录 :arrow_heading_up:](https:\u002F\u002Fgithub.com\u002Fkmario23\u002Fdeep-learning-drizzle#contents) \n\n\n| -1. | **用于语音识别的深度学习** | 许多传奇人物，AoE | `无` | [YouTube视频](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLM4Pv4KYYzGyFYCXV6YPWAKVOR2gmHnQd) | 2015-2018 |\n\n### :fire: 现代计算机视觉 :camera_flash: :movie_camera:\n\n| 序号 | 课程名称 | 大学\u002F讲师 | 课程网页 | 讲座视频 | 年份 |\n| ---- | ------------------------------------------------------------ | ------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | ---------- |\n| 1. | **微软计算机视觉暑期学校** - （经典） | 众多传奇人物，莫斯科国立大学 | `无` | [YouTube视频](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLbwKcm5vdiSYU54xFUG1zoxQTulqvIcJu) \u003Cbr> [俄语镜像](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL-_cKNuVAYAUp0eCL7KO8QY4ETY3tIDFH) | 2011 |\n| 2. | **计算机视觉** - （经典） | 穆巴拉克·沙赫，中佛罗里达大学 | [CAP-5415](http:\u002F\u002Fcrcv.ucf.edu\u002Fcourses\u002FCAP5415\u002FFall2012\u002Findex.php) | [YouTube讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLd3hlSJsX_Imk_BPmB_H3AQjFKZS9XgZm) | 2012 |\n| 3. | **图像与多维信号处理** - （经典） | 威廉·霍夫，科罗拉多矿业学院 | [CSCI 510\u002FEENG 510](http:\u002F\u002Finside.mines.edu\u002F~whoff\u002Fcourses\u002FEENG510) | [YouTube讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLyED3W677ALNv8Htn0f9Xh-AHe1aZPftv) | 2012 |\n| 4. | **计算机视觉** - （经典） | 威廉·霍夫，科罗拉多矿业学院 | [CSCI 512\u002FEENG 512](http:\u002F\u002Finside.mines.edu\u002F~whoff\u002Fcourses\u002FEENG512\u002Findex.htm) | [YouTube讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL4B3F8D4A5CAD8DA3) | 2012 |\n| 5. | **图像与视频处理：从火星到好莱坞，途中停靠医院** | 吉列尔莫·萨皮罗，杜克大学 | `无` | [YouTube视频](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLZ9qNFMHZ-A79y1StvUUqgyL-O0fZh2rs) | 2013 |\n| 6. | **多视图几何**（经典） | 丹尼尔·克雷默斯，慕尼黑工业大学 | [mvg](https:\u002F\u002Fvision.in.tum.de\u002Fteaching\u002Fss2014\u002Fmvg2014) | [YouTube讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLTBdjV_4f-EJn6udZ34tht9EVIW7lbeo4) | 2013 |\n| 7. | **机器人、视觉与图形学的数学方法** | 贾斯汀·所罗门，斯坦福大学 | [CS-205A](http:\u002F\u002Fgraphics.stanford.edu\u002Fcourses\u002Fcs205a\u002F) | [YouTube讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLQ3UicqQtfNvQ_VzflHYKhAqZiTxOkSwi) | 2013 |\n| 8. | **计算机视觉** - （经典） | 穆巴拉克·沙赫，中佛罗里达大学 | [CAP-5415](http:\u002F\u002Fcrcv.ucf.edu\u002Fcourses\u002FCAP5415\u002FFall2014\u002Findex.php) | [YouTube讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLd3hlSJsX_ImKP68wfKZJVIPTd8Ie5u-9) | 2014 |\n| 9. | **用于视觉特效的计算机视觉**（经典） | 瑞奇·拉德克，伦斯勒理工学院 | [ECSE-6969](https:\u002F\u002Fwww.ecse.rpi.edu\u002F~rjradke\u002Fcvfxcourse.html) | [YouTube讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLuh62Q4Sv7BUJlKlt84HFqSWfW36MDd5a) | S2014 |\n| 10. | **飞行机器人的自主导航** | 尤尔根·施图姆，慕尼黑工业大学 | [Autonavx](https:\u002F\u002Fjsturm.de\u002Fwp\u002Fteaching\u002Fautonavx-slides\u002F) | [YouTube讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLTBdjV_4f-EKBCUs1HmMtsnXv4JUoFrzg) | 2014 |\n| | | | | | |\n| 11. | **SLAM - 移动机器人技术** | 西里尔·斯塔赫尼斯，弗莱堡大学 | [RobotMapping](http:\u002F\u002Fais.informatik.uni-freiburg.de\u002Fteaching\u002Fws13\u002Fmapping\u002F) | [YouTube讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLgnQpQtFTOGQrZ4O5QzbIHgl3b1JHimN_) | 2014 |\n| 12. | **计算摄影学** | 伊尔凡·埃萨、大卫·乔伊纳、阿尔潘·查克拉博蒂 | [CP-Udacity](https:\u002F\u002Feu.udacity.com\u002Fcourse\u002Fcomputational-photography--ud955) | [YouTube讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLAwxTw4SYaPn-unAWtRMleY4peSe4OzIY) | 2015 |\n| 13. | **数字图像处理导论** | 瑞奇·拉德克，伦斯勒理工学院 | [ECSE-4540](https:\u002F\u002Fwww.ecse.rpi.edu\u002F~rjradke\u002Fimproccourse.html) | [YouTube讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLuh62Q4Sv7BUf60vkjePfcOQc8sHxmnDX) | S2015 |\n| 14. | **数字摄影讲座** | 马克·列沃伊，斯坦福大学\u002F谷歌研究 | [LoDP](https:\u002F\u002Fsites.google.com\u002Fsite\u002Fmarclevoylectures\u002F) | [YouTube讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL7ddpXYvFXspUN0N-gObF1GXoCA-DA-7i) | 2016 |\n| 15. | **计算机视觉导论**（基础） | 亚伦·鲍比克、伊尔凡·埃萨、阿尔潘·查克拉博蒂 | [CV-Udacity](https:\u002F\u002Feu.udacity.com\u002Fcourse\u002Fintroduction-to-computer-vision--ud810) | [YouTube讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLAwxTw4SYaPnbDacyrK_kB_RUkuxQBlCm) | 2016 |\n| 16. | **计算机视觉** | 赛义德·阿法克·阿里·沙赫，西澳大利亚大学 | `无` | [YouTube讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLvqB6_mDBCdlnT84LK_NvbOqcXLlOTR8j) | 2016 |\n| 17. | **摄影测量 I & II** | 西里尔·斯塔赫尼斯，波恩大学 | [PG-I&II](https:\u002F\u002Fwww.ipb.uni-bonn.de\u002Fphotogrammetry-i-ii\u002F) | [YouTube讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLgnQpQtFTOGRsi5vzy9PiQpNWHjq-bKN1) | 2016 |\n| 18. | **面向计算机视觉的深度学习** | 巴塞罗那理工大学 | [DLCV-16](http:\u002F\u002Fimatge-upc.github.io\u002Ftelecombcn-2016-dlcv\u002F) \u003Cbr\u002F> [DLCV-17](https:\u002F\u002Ftelecombcn-dl.github.io\u002F2017-dlcv\u002F) \u003Cbr\u002F> [DLCV-18](https:\u002F\u002Ftelecombcn-dl.github.io\u002F2018-dlcv\u002F) | [YouTube讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL-5eMc3HQTBbuaTFP4wsfD2Y2VqEfQcaP) | 2016-2018 |\n| 19. | **卷积神经网络** | 安德鲁·吴，斯坦福大学 | [DeepLearning.AI](https:\u002F\u002Fwww.deeplearning.ai\u002Fdeep-learning-specialization\u002F) | [YouTube讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLkDaE6sCZn6Gl29AoE31iwdVwSG-KnDzF) | 2017 |\n| 20. | **用于计算机视觉的变分方法** | 丹尼尔·克雷默斯，慕尼黑工业大学 | [VMCV](https:\u002F\u002Fvision.in.tum.de\u002Fteaching\u002Fws2016\u002Fvmcv2016) | [YouTube讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLTBdjV_4f-EJ7A2iIH5L5ztqqrWYjP2RI) | 2017 |\n| | | | | | |\n| 21. | **计算机视觉冬季学校** | 众多传奇人物，以色列高等研究院 | [WS-CV](http:\u002F\u002Fwww.as.huji.ac.il\u002Fcse) | [YouTube讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLTn74Qx5mPsSniA5tt6W-o0OGYEeKScug) | 2017 |\n| 22. | **面向视觉计算的深度学习** | 德布杜特·希特，印度理工学院卡普尔分校 | [Nptel](https:\u002F\u002Fonlinecourses.nptel.ac.in\u002Fnoc18_ee08\u002Fpreview) [笔记本](https:\u002F\u002Fgithub.com\u002Fiitkliv\u002Fdlvcnptel) | [YouTube讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLuv3GM6-gsE1Biyakccxb3FAn4wBLyfWf) | 2018 |\n| 23. | **计算机视觉的古老秘密** | 约瑟夫·雷德蒙、阿里·法哈迪 | [TASCV](https:\u002F\u002Fpjreddie.com\u002Fcourses\u002Fcomputer-vision\u002F) ; [TASCV-UW](https:\u002F\u002Fcourses.cs.washington.edu\u002Fcourses\u002Fcse455\u002F18sp\u002F) | [YouTube讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLjMXczUzEYcHvw5YYSU92WrY8IwhTuq7p) | 2018 |\n| 24. | **现代机器人技术** | 凯文·林奇，西北大学机器人实验室 | [modern-robot](http:\u002F\u002Fhades.mech.northwestern.edu\u002Findex.php\u002FModern_Robotics) | [YouTube讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLggLP4f-rq02vX0OQQ5vrCxbJrzamYDfx) | 2018 |\n| 25. | **数字图像处理** | 亚历克斯·布朗斯坦，以色列理工学院 | [CS236860](https:\u002F\u002Fvistalab-technion.github.io\u002Fcs236860\u002Finfo\u002F) | [YouTube讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLM0a6Z788YAZOxUyWda9y3N_i2upIj1Ep) | 2018 |\n| 26. | **成像的数学** - 成像中的变分方法与优化 | 众多传奇人物，亨利·庞加莱研究所 | [研讨会-1](http:\u002F\u002Fwww.ihp.fr\u002Fsites\u002Fdefault\u002Ffiles\u002Fconf1-04_au_08_fevr-imaging2019.pdf) | [YouTube讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL9kd4mpdvWcAzD5Aq-P1TrLLiYckrloxw) | 2019 |\n| 27. | **面向视频的深度学习** | 哈维尔·吉罗，巴塞罗那理工大学 | [deepvideo](https:\u002F\u002Fmcv-m6-video.github.io\u002Fdeepvideo-2019\u002F) | [YouTube讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL-5eMc3HQTBbPY-627Gornj09pZrNQgPD) | 2019 |\n| 28. | **形状与成像的统计建模** | 众多传奇人物，巴黎亨利·庞加莱研究所 | [研讨会-2](https:\u002F\u002Fimaging-in-paris.github.io\u002Fsemester2019\u002Fworkshop2prog) | [YouTube讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL9kd4mpdvWcAzD5Aq-P1TrLLiYckrloxw) | 2019 |\n| 29. | **成像与机器学习** | 众多传奇人物，巴黎亨利·庞加莱研究所 | [研讨会-3](https:\u002F\u002Fimaging-in-paris.github.io\u002Fsemester2019\u002Fworkshop3prog) | [YouTube讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL9kd4mpdvWcAzD5Aq-P1TrLLiYckrloxw) | 2019 |\n| 30. | **计算机视觉** | 贾扬塔·穆克霍帕迪亚伊，印度理工学院卡普尔分校 | [CV-nptel](https:\u002F\u002Fnptel.ac.in\u002Fcourses\u002F106\u002F105\u002F106105216\u002F) | [YouTube讲座](https:\u002F\u002Fnptel.ac.in\u002Fcourses\u002F106\u002F105\u002F106105216\u002F) | 2019 |\n| | | | | | |\n| 31. | **面向计算机视觉的深度学习** | 贾斯汀·约翰逊，密歇根大学 | [EECS 498-007](https:\u002F\u002Fweb.eecs.umich.edu\u002F~justincj\u002Fteaching\u002Feecs498\u002F) | [讲座视频](http:\u002F\u002Fleccap.engin.umich.edu\u002Fleccap\u002Fsite\u002Fjhygcph151x25gjj1f0) \u003Cbr\u002F> [YouTube讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL5-TkQAfAZFbzxjBHtzdVCWE0Zbhomg7r) | 2019 |\n| 32. | **传感器与状态估计 2** | 西里尔·斯塔赫尼斯，波恩大学 | `无` | [YouTube讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLgnQpQtFTOGQh_J16IMwDlji18SWQ2PZ6) | S2020 |\n| 33. | **计算机视觉 III：检测、分割与跟踪** | 劳拉·莱阿尔-泰谢，慕尼黑工业大学 | [CV3DST](https:\u002F\u002Fdvl.in.tum.de\u002Fteaching\u002Fcv3dst-ss20\u002F) | [YouTube讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLog3nOPCjKBneGyffEktlXXMfv1OtKmCs) | S2020 |\n| 34. | **面向计算机视觉的高级深度学习** | 劳拉·莱阿尔-泰谢和马蒂亚斯·尼瑟纳，慕尼黑工业大学 | [ADL4CV](https:\u002F\u002Fdvl.in.tum.de\u002Fteaching\u002Fadl4cv-ss20) | [YouTube讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLog3nOPCjKBnjhuHMIXu4ISE4Z4f2jm39) | S2020 |\n| 35. | **计算机视觉：基础** | 弗雷德·汉普雷希特，海德堡大学 | [CVF](https:\u002F\u002Fhci.iwr.uni-heidelberg.de\u002Fial\u002Fcvf) | [YouTube讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLuRaSnb3n4kRAbnmiyGd77hyoGzO9wPde) | SS2020 |\n| 36. | **MIT 视觉研讨会** | 众多传奇人物，麻省理工学院 | [MIT-Vision](https:\u002F\u002Fsites.google.com\u002Fview\u002Fvisionseminar\u002Fpast-talks) | [YouTube讲座](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCLMiFkFyfcNnZs6iwYLPI9g\u002Fvideos) | 2015-至今 |\n| 37. | **TUM AI 客座讲座** | 众多传奇人物，慕尼黑工业大学 | [TUM-AI](https:\u002F\u002Fniessner.github.io\u002FTUM-AI-Lecture-Series) | [YouTube讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLQ8Y4kIIbzy8kMlz7cRqz-BjbdyWsfLXt) | 2020 - 至今 |\n| 38. | **3D 几何与视觉研讨会** | 众多传奇人物，线上 | [3DGV seminar](https:\u002F\u002F3dgv.github.io) | [YouTube讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLZk0jtN0g8e-xVTfsiV67q8Iz1cZO_FpV) | 2020 - 至今 |\n| 39. | **基于事件的机器人视觉** | 吉列尔莫·加列戈，柏林工业大学 | [EVIS-SS20](https:\u002F\u002Fsites.google.com\u002Fview\u002Fguillermogallego\u002Fteaching\u002Fevent-based-robot-vision) | [YouTube讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL03Gm3nZjVgUFYUh3v5x8jVonjrGfcal8) | 2020 - 至今 |\n| 40. | **面向计算机视觉的深度学习** | 维尼思·巴拉苏布拉马尼安，印度理工学院海得拉巴分校 | [DL-CV'20](https:\u002F\u002Fonlinecourses.nptel.ac.in\u002Fnoc20_cs88\u002Fpreview) | [YouTube讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLyqSpQzTE6M_PI-rIz4O1jEgffhJU9GgG) | 2020 |\n| | | | | | |\n| 41. | **面向视觉计算的深度学习** | 彼得·翁卡，沙特阿拉伯国王阿卜杜拉科技大学 | `无` | [YouTube讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLMpQLEui13s2DHbw6kTTxwQma8rehlfZE) | 2020 |\n| 42. | **计算机视觉** | 约格什·拉瓦特，中佛罗里达大学 | [CAP5415-CV](https:\u002F\u002Fwww.crcv.ucf.edu\u002Fcourses\u002Fcap5415-fall-2020\u002Fschedule\u002F) | [YouTube讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLd3hlSJsX_Ikm5il1HgmDB_z62BeoikFX) | F2020 |\n| 43. | **多媒体信号处理** | 马克·哈塞加瓦-约翰逊，伊利诺伊大学 | [ECE-417 MSP](https:\u002F\u002Fcourses.engr.illinois.edu\u002Fece417\u002Ffa2020\u002F) | [讲座视频](https:\u002F\u002Fmediaspace.illinois.edu\u002Fchannel\u002FECE%20417\u002F26816181) | F2020 |\n| 44. | **计算机视觉** | 安德烈亚斯·盖格尔，图宾根大学 | [Comp.Vis](https:\u002F\u002Funi-tuebingen.de\u002Ffakultaeten\u002Fmathematisch-naturwissenschaftliche-fakultaet\u002Ffachbereiche\u002Finformatik\u002Flehrstuehle\u002Fautonomous-vision\u002Flectures\u002Fcomputer-vision\u002F) | [YouTube讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL05umP7R6ij35L2MHGzis8AEHz7mg381_) | S2021 |\n| 45. | **3D 计算机视觉** | 李金熙，新加坡国立大学 | `无` | [YouTube讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLxg0CGqViygP47ERvqHw_v7FVnUovJeaz) | 2021 |\n| 46. | **面向计算机视觉的深度学习：基础与应用** | T. 德凯尔等人，魏茨曼科学研究所 | [DL4CV](https:\u002F\u002Fdl4cv.github.io\u002Fschedule.html) | [YouTube讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL_Z2_U9MIJdNgFM7-f2fZ9ZxjVRP_jhJv) | S2021 |\n| 47. | **3D 和几何深度学习中的当前机器学习课题** | 阿尼梅什·加格及其他，多伦多大学 | [CSC 2547](http:\u002F\u002Fwww.pair.toronto.edu\u002Fcsc2547-w21) | [YouTube讲座](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCrsmAXnwu6sgccWevW12Dfg\u002Fvideos) | 2021 |\n| 48. | **计算机视觉的第一性原理** | 施里·K·纳亚尔，哥伦比亚大学 | [FPCV](https:\u002F\u002Ffpcv.cs.columbia.edu) | [YouTube讲座](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCf0WB91t8Ky6AuYcQV0CcLw\u002Fvideos) | 2021 |\n| 49. | **自动驾驶汽车** | 安德烈亚斯·盖格尔，图宾根大学 | [SDC'21](https:\u002F\u002Funi-tuebingen.de\u002Fde\u002F123611) | [YouTube讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL05umP7R6ij321zzKXK6XCQXAaaYjQbzr) | W2021 |\n| | | | | | |\n\n[前往目录 :arrow_heading_up:](https:\u002F\u002Fgithub.com\u002Fkmario23\u002Fdeep-learning-drizzle#contents) \n\n:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign……（共60个减号）:heavy_minus_sign:\n\n### :star2: 训练营或暑期学校 :maple_leaf:\n:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign……（共60个减号）:heavy_minus_sign:\n\n| 序号 | 课程名称 | 大学\u002F讲师 | 课程网页 | 讲座视频 | 年份 |\n| ---- | ------------------------------------------------------- | -------------------------------------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | --------- |\n| 1. | **深度学习、特征学习** | 众多名家，IPAM UCLA | [GSS-2012](https:\u002F\u002Fwww.ipam.ucla.edu\u002Fprograms\u002Fsummer-schools\u002Fgraduate-summer-school-deep-learning-feature-learning\u002F) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLHyI3Fbmv0SdzMHAy0aN59oYnLy5vyyTA) | 2012 |\n| 2. | **大数据训练营** | 众多名家，西蒙斯研究所 | [大数据](https:\u002F\u002Fsimons.berkeley.edu\u002Fworkshops\u002Fschedule\u002F316) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLgKuh-lKre13RmUC2AybRvVAxO5DEMIBH) | 2013 |\n| 3. | **机器学习暑期学校** | 众多名家，图宾根MPI-IS | [MLSS-13](http:\u002F\u002Fmlss.tuebingen.mpg.de\u002F2013\u002F2013\u002Findex.html) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLqJm7Rc5-EXFv6RXaPZzzlzo93Hl0v91E) | 2013 |\n| 4. | **研究生暑期学校：计算机视觉** | 众多名家，IPAM-UCLA | [GSS-CV](http:\u002F\u002Fwww.ipam.ucla.edu\u002Fprograms\u002Fsummer-schools\u002Fgraduate-summer-school-computer-vision\u002F) | [视频讲座](http:\u002F\u002Fwww.ipam.ucla.edu\u002Fprograms\u002Fsummer-schools\u002Fgraduate-summer-school-computer-vision\u002F?tab=schedule) | 2013 |\n| 5. | **机器学习暑期学校** | 众多名家，雷克雅未克大学 | [MLSS-14](http:\u002F\u002Fmlss2014.hiit.fi\u002F) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLqdbxUnkqOw2nKn7VxYqIrKWcqRkQYOsF) | 2014 |\n| 6. | **机器学习暑期学校** | 众多名家，匹兹堡 | [MLSS-14](http:\u002F\u002Fwww.mlss2014.com) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLZSO_6-bSqHQCIYxE3ycGLXHMjK3XV7Iz) | 2014 |\n| 7. | **深度学习暑期学校** | 众多名家，蒙特利尔大学 | [DLSS-15](https:\u002F\u002Fsites.google.com\u002Fsite\u002Fdeeplearningsummerschool\u002Fhome) | [YouTube-讲座](http:\u002F\u002Fvideolectures.net\u002Fdeeplearning2015_montreal\u002F) | 2015 |\n| 8. | **生物医学图像分析暑期学校** | 众多名家，巴黎中央理工学院 | `无` | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLgSHH6boFf5uJAUT4ZRiAZc_ofXolkAGK) | 2015 |\n| 9. | **信号处理的数学** | 众多名家，豪斯多夫数学研究所 | [SigProc](http:\u002F\u002Fwww.him.uni-bonn.de\u002Fsignal-processing-2016\u002F) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLul8LCT3AJqSQo3lr5RbwxJ92RsgRuDtx) | 2016 |\n| 10. | **微软研究院——机器学习课程** | S V N Vishwanathan 和 Prateek Jain MS-Research | `无` | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL34iyE0uXtxo7vPXGFkmm6KbgZQwjf9Kf) | 2016 |\n| | | | | | |\n| 11. | **深度学习暑期学校** | 众多名家，蒙特利尔大学 | [DL-SS-16](https:\u002F\u002Fsites.google.com\u002Fsite\u002Fdeeplearningsummerschool2016\u002Fhome) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL5bqIc6XopCbb-FvnHmD1neVlQKwGzQyR) | 2016 |\n| 12. | **里斯本机器学习学校** | 众多名家，葡萄牙高等技术学院 | [LxMLS-16](http:\u002F\u002Flxmls.it.pt\u002F2016\u002F) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLToLj8M4ao-fymxXBIOU6sF1NGFLb5EiX) | 2016 |\n| 13. | **机器学习进展与应用研讨会** | 众多名家，多伦多大学菲尔兹研究所 | [MLAAS-16](http:\u002F\u002Fwww.fields.utoronto.ca\u002Factivities\u002F16-17\u002Fmachine-learning) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLfsVAYSMwskuQcRkuDApP40lX_i08d0QK) \u003Cbr\u002F> [视频讲座](http:\u002F\u002Fwww.fields.utoronto.ca\u002Fvideo-archive\u002Fevent\u002F2267) | 2016-2017 |\n| 14. | **机器学习进展与应用研讨会** | 众多名家，多伦多大学菲尔兹研究所 | [MLAAS-17](http:\u002F\u002Fwww.fields.utoronto.ca\u002Factivities\u002F17-18\u002Fmachine-learning) | [视频讲座](http:\u002F\u002Fwww.fields.utoronto.ca\u002Fvideo-archive\u002Fevent\u002F2487) | 2017-2018 |\n| 15. | **机器学习暑期学校** | 众多名家，图宾根MPI-IS | [MLSS-17](http:\u002F\u002Fmlss.tuebingen.mpg.de\u002F2017\u002Findex.html) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLqJm7Rc5-EXFUOvoYCdKikfck8YeUCnl9) | 2017 |\n| 16. | **表示学习** | 众多名家，西蒙斯研究所 | [RepLearn](https:\u002F\u002Fsimons.berkeley.edu\u002Fworkshops\u002Fabstracts\u002F3750) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLgKuh-lKre13UNV4ztsWUXciUZ7x_ZDHz) | 2017 |\n| 17. | **机器学习基础** | 众多名家，西蒙斯研究所 | [ML-BootCamp](https:\u002F\u002Fsimons.berkeley.edu\u002Fworkshops\u002Fabstracts\u002F3748) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLgKuh-lKre11GbZWneln-VZDLHyejO7YD) | 2017 |\n| 18. | **优化、统计与不确定性** | 众多名家，西蒙斯研究所 | [Optim-Stats](https:\u002F\u002Fsimons.berkeley.edu\u002Fworkshops\u002Fabstracts\u002F4795) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLgKuh-lKre13ACD44z2FH-IVP1e8ip5JO) | 2017 |\n| 19. | **深度学习：理论、算法与应用** | 众多名家，柏林工业大学 | [DL: TAA](http:\u002F\u002Fdoc.ml.tu-berlin.de\u002Fdlworkshop2017\u002F) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLJOzdkh8T5kqCNV_v1w2tapvtJDZYiohW) | 2017 |\n| 20. | **深度学习与强化学习暑期学校** | 众多名家，蒙特利尔大学 | [DLRL-2017](https:\u002F\u002Fmila.quebec\u002Fen\u002Fcours\u002Fdeep-learning-summer-school-2017\u002F) | [讲座视频](http:\u002F\u002Fvideolectures.net\u002Fdeeplearning2017_montreal\u002F) | 2017 |\n| | | | | | |\n| 21. | **机器学习中的统计物理方法** | 众多名家，印度塔塔基础科学研究所国际理论科学中心 | [SPMML](https:\u002F\u002Fwww.icts.res.in\u002Fdiscussion-meeting\u002FSPMML2017) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL04QVxpjcnjhtL3IIVyFRMOgdhWtPn7YJ) | 2017 |\n| 22. | **里斯本机器学习学校** | 众多名家，葡萄牙高等技术学院 | [LxMLS-17](http:\u002F\u002Flxmls.it.pt\u002F2017\u002F) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLToLj8M4ao-fuRfnzEJCCnvuW2_FeJ73N) | 2017 |\n| 23. | **交互式学习** | 众多名家，西蒙斯研究所，伯克利 | [IL-2017](https:\u002F\u002Fsimons.berkeley.edu\u002Fworkshops\u002Fschedule\u002F3749) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLgKuh-lKre10T2POF-WzXh0ckdpyvANUx) | 2017 |\n| 24. | **机器学习中的计算挑战** | 众多名家，西蒙斯研究所，伯克利 | [CCML-17](https:\u002F\u002Fsimons.berkeley.edu\u002Fworkshops\u002Fschedule\u002F3751) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLgKuh-lKre12eXz4dnvc8oervo2_Af4iU) | 2017 |\n| 25. | **数据科学基础** | 众多名家，西蒙斯研究所 | [DS-BootCamp](https:\u002F\u002Fsimons.berkeley.edu\u002Fworkshops\u002Fabstracts\u002F6680) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLgKuh-lKre13r1Qrnrejj3f498-NurSf3) | 2018 |\n| 26. | **深度学习与贝叶斯方法** | 众多名家，莫斯科高等经济学院 | [DLBM-SS](http:\u002F\u002Fdeepbayes.ru\u002F2018\u002F) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLe5rNUydzV9Q01vWCP9BV7NhJG3j7mz62) | 2018 |\n| 27. | **新的深度学习技术** | 众多名家，IPAM UCLA | [IPAM-研讨会](https:\u002F\u002Fwww.ipam.ucla.edu\u002Fprograms\u002Fworkshops\u002Fnew-deep-learning-techniques\u002F?tab=schedule) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLHyI3Fbmv0SdM0zXj31HWjG9t9Q0v2xYN) | 2018 |\n| 28. | **深度学习与强化学习暑期学校** | 众多名家，多伦多大学 | [DLRL-2018](https:\u002F\u002Fdlrlsummerschool.ca\u002F2018-event\u002F) | [讲座视频](http:\u002F\u002Fvideolectures.net\u002FDLRLsummerschool2018_toronto\u002F) | 2018 |\n| 29. | **机器学习暑期学校** | 众多名家，西班牙马德里自治大学 | [MLSS-18](http:\u002F\u002Fmlss.ii.uam.es\u002Fmlss2018\u002Findex.html) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCbPJHr__eIor_7jFH3HmVHQ\u002Fvideos) \u003Cbr\u002F> [课程视频](http:\u002F\u002Fmlss.ii.uam.es\u002Fmlss2018\u002Fspeakers.html) | 2018 |\n| 30. | **机器学习的理论基础** | 众多名家，印度塔塔基础科学研究所国际理论科学中心 | [TBML-18](https:\u002F\u002Fwww.icts.res.in\u002Fdiscussion-meeting\u002Ftbml2018) | [讲座视频](https:\u002F\u002Fwww.icts.res.in\u002Fdiscussion-meeting\u002Ftbml2018\u002Ftalks) \u003Cbr\u002F> [YouTube-视频](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL04QVxpjcnjj1DgnXxFBo2fkSju4r-ggr) | 2018 |\n| | | | | | |\n| 31. | **波兰视角下的机器学习** | 众多名家，华沙 | [PLinML-18](https:\u002F\u002Fplinml.mimuw.edu.pl\u002F) | [YouTube-视频](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLoaWrlj9TDhPcA6N9dZQ6GPXboYuumDRp) | 2018 |\n| 32. | **天文学中的大数据分析** | 众多名家，特内里费岛 | [BDAA-18](http:\u002F\u002Fresearch.iac.es\u002Fwinterschool\u002F2018\u002Fpages\u002Fbook-ws2018.php) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLM4Pv4KYYzGx42W5pSp3Itetp0u-PENtI) | 2018 |\n| 33. | **机器学习进展与应用研讨会** | 众多名家，多伦多大学菲尔兹研究所 | [MLASS](http:\u002F\u002Fwww.fields.utoronto.ca\u002Factivities\u002F18-19\u002Fmachine-learning) | [视频讲座](http:\u002F\u002Fwww.fields.utoronto.ca\u002Fvideo-archive\u002Fevent\u002F2681) | 2018-2019 |\n| 34. | **MIFODS- ML, Stats, ToC研讨会** | 众多名家，麻省理工学院 | [MIFODS-研讨会](http:\u002F\u002Fmifods.mit.edu\u002Fseminar.php) | [讲座视频](http:\u002F\u002Fmifods.mit.edu\u002Fseminar.php) | 2018-2019 |\n| 35. | **学习机器系列研讨会** | 众多名家，康奈尔科技 | [LMSS](https:\u002F\u002Flmss.tech.cornell.edu\u002F) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLycW2Yy79JuxbQZ9uHEu_NS3cGNomhL2A) | 2018-至今 |\n| 36. | **机器学习暑期学校** | 众多名家，南非 | [MLSS'19](https:\u002F\u002Fmlssafrica.com\u002Fprogramme-schedule\u002F) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUC722CmQVgcLtxt_jXr3RyWg\u002Fvideos) | 2019 |\n| 37. | **深度学习训练营** | 众多名家，西蒙斯研究所，伯克利 | [DLBC-19](https:\u002F\u002Fsimons.berkeley.edu\u002Fworkshops\u002Fschedule\u002F10624) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLgKuh-lKre12c2Il9mNX0Cmp9Z4oFNrQh) | 2019 |\n| 38. | **深度学习前沿** | 众多名家，西蒙斯研究所，伯克利 | [FoDL-19](https:\u002F\u002Fsimons.berkeley.edu\u002Fworkshops\u002Fschedule\u002F10627) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLgKuh-lKre11ekU7g-Z_qsvjDD8cT-hi9) | 2019 |\n| 39. | **数据的数学：用于感知、近似和学习的结构化表示** | 众多名家，伦敦艾伦·图灵研究所 | [MoD-19](https:\u002F\u002Fwww.turing.ac.uk\u002Fsites\u002Fdefault\u002Ffiles\u002F2019-05\u002Fagenda_9_3.pdf) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLuD_SqLtxSdX_w1Ztexpzl_EJgFQSkWez) | 2019 |\n| 40. | **深度学习与贝叶斯方法** | 众多名家，莫斯科高等经济学院 | [DLBM-SS](http:\u002F\u002Fdeepbayes.ru\u002F) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLe5rNUydzV9He8VDStpU0o8Yp63OecdW) | 2019 |\n| | | | | | |\n| 41. | **深度学习与数据科学的数学** | 众多名家，剑桥艾萨克·牛顿研究所 | [MoDL-DS](https:\u002F\u002Fgateway.newton.ac.uk\u002Fevent\u002Fofbw46) | [讲座视频](https:\u002F\u002Fgateway.newton.ac.uk\u002Fevent\u002Fofbw46\u002Fprogramme) | 2019 |\n| 42. | **深度学习的几何学** | 众多名家，微软雷德蒙德研究院 | [GoDL](https:\u002F\u002Fwww.microsoft.com\u002Fen-us\u002Fresearch\u002Fevent\u002Fai-institute-2019) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLD7HFcN7LXRe30qq36It2XCljxc340O_d) | 2019 |\n| 43. | **面向科学的深度学习学校** | 许多人，劳伦斯伯克利国家实验室，伯克利 | [DLfSS](https:\u002F\u002Fdl4sci-school.lbl.gov\u002Fagenda) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL20S5EeApOSvfvEyhCPOUzU7zkBcR5-eL) | 2019 |\n| 44. | **深度学习中的新兴挑战** | 众多名家，西蒙斯研究所，伯克利 | [ECDL](https:\u002F\u002Fsimons.berkeley.edu\u002Fworkshops\u002Fschedule\u002F10629) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLgKuh-lKre10BpafDrv0fg2VNUweWXWVd) | 2019 |\n| 45. | **全栈深度学习** | Pieter Abbeel和其他许多人，加州大学伯克利分校 | [FSDL-M19](https:\u002F\u002Ffullstackdeeplearning.com\u002Fmarch2019) | [YouTube-讲座-第一天](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL1T8fO7ArWlcf3Hc4VMEVBlH8HZm_NbeB) \u003Cbr\u002F> [第二天](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL1T8fO7ArWlf6TWwdstb-PcwlubnlrKrm) | 2019 |\n| 46. | **机器学习的算法与理论方面** | 众多传奇人物，班加罗尔IIIT | [ACM-ML](https:\u002F\u002Findia.acm.org\u002Feducation\u002Fmachine-learning) \u003Cbr\u002F> [nptel](https:\u002F\u002Fnptel.ac.in\u002Fcourses\u002F128\u002F106\u002F128106011\u002F) | [YouTube-讲座](https:\u002F\u002Fnptel.ac.in\u002Fcourses\u002F128\u002F106\u002F128106011) | 2019 |\n| 47. | **深度学习与强化学习暑期学校** | 众多名家，加拿大埃德蒙顿AMII | [DLRL-2019](https:\u002F\u002Fdlrlsummerschool.ca\u002Fpast-years) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLKlhhkvvU8-aXmPQZNYG_e-2nTd0tJE8v) | 2019 |\n| 48. | **机器学习的数学** - 夏季研究生院 | 众多名家，华盛顿大学 | [MoML-SGS](http:\u002F\u002Fwww.msri.org\u002Fsummer_schools\u002F866#schedule), [MoML-SS](http:\u002F\u002Fmathofml.cs.washington.edu\u002F) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLTPQEx-31JXhguCush5J7OGnEORofoCW9) | 2019 |\n| 49. | **深度学习理论研讨会：下一步在哪里？** | 众多名家，普林斯顿高等研究院 | [WTDL](https:\u002F\u002Fwww.math.ias.edu\u002Fwtdl) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLdDZb3TwJPZ5dqqg_S-rgJqSFeH4DQqFQ) | 2019 |\n| 50. | **计算视觉暑期学校** | 众多名家，德国黑森林 | [CVSS-2019](http:\u002F\u002Forga.cvss.cc\u002Fprogram-cvss-2019\u002F) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLeCNfJWZKqxsvidOlVLtWq9s7sIsX1QTC) | 2019 |\n| | | | | | |\n| 51. | **复杂结构下的学习** | 众多名家，MIT | [LUCS](https:\u002F\u002Fmifods.mit.edu\u002Fcomplex.php) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLM4Pv4KYYzGwhIHcaY6zYR7M9hhFO4Vud) | 2020 |\n| 52. | **机器学习暑期学校** | 众多名家，图宾根MPI-IS（线上） | [MLSS](http:\u002F\u002Fmlss.tuebingen.mpg.de\u002F2020\u002Fschedule.html) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCBOgpkDhQuYeVVjuzS5Wtxw\u002Fvideos) | SS2020 |\n| 53. | **东欧机器学习暑期学校** | 众多名家，克拉科夫，波兰（线上） | [EEML](https:\u002F\u002Fwww.eeml.eu\u002Fprogram) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLaKY4p4V3gE1j01FOY2FeglV4jRntQj84) | S2020 |\n| 54. | **里斯本机器学习暑期学校** | 众多名家，里斯本，葡萄牙（线上） | [LxMLS](http:\u002F\u002Flxmls.it.pt\u002F2020\u002F?page_id=19) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCkVFZWgT1jR75UvSLGP9_mw) | S2020 |\n| 55. | **优化、统计与机器学习新方向研讨会** | 众多名家，普林斯顿高等研究院 | [ML-Opt 新方向](https:\u002F\u002Fwww.ias.edu\u002Fvideo\u002Fworkshop\u002F2020\u002F0415-16) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLdDZb3TwJPZ4Ri6i0MIdesIEpYK4lx17Q) | 2020 |\n| 56. | **地中海机器学习学校** | 众多名家，意大利（线上） | [M2L-school](https:\u002F\u002Fwww.m2lschool.org\u002Ftalks) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLF-wkqRv4u1YRbfnwN8cXXyrmXld-sked) | 2021 |\n| 57. | **机器学习数学——全球研讨会** | 众多名家，虚拟形式 | [1W-ML](https:\u002F\u002Fsites.google.com\u002Fview\u002Foneworldml\u002Fpast-events) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCz7WlgXs20CzugkfxhFCNFg\u002Fvideos) | 2020 - 至今 |\n| 58. | **深度学习理论暑期学校** | 众多名家，普林斯顿大学（线上） | [DLT'21](https:\u002F\u002Fdeep-learning-summer-school.princeton.edu) | [YouTube-讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL2mB9GGlueJj_FNjJ8RWgz4Nc_hCSXfMU) | 2021 |\n| | | | | | |\n\n[前往目录 :arrow_heading_up:](https:\u002F\u002Fgithub.com\u002Fkmario23\u002Fdeep-learning-drizzle#contents)\n\n:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:\n\n\n### :bird: 人工智能概览 :eagle:\n:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:\n\n| 序号 | 课程名称 | 大学\u002F讲师 | 课程网页 | 讲座视频 | 年份 |\n| ---- | -------------------------------------- | -------------------------------------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | --------- |\n| 1. | **通用人工智能** | 众多传奇人物，麻省理工学院 | [6.S099-AGI](https:\u002F\u002Fagi.mit.edu\u002F) | [讲座视频](https:\u002F\u002Fagi.mit.edu\u002F) | 2018-2019 |\n| 2. | **AI播客** | 众多传奇人物，麻省理工学院 | [AI-Pod](https:\u002F\u002Flexfridman.com\u002Fai\u002F) | [YouTube讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLrAXtmErZgOdP_8GztsuKi9nrraNbKKp4) | 2018-2019 |\n| 3. | **NYU - AI研讨会** | 众多传奇人物，纽约大学 | [modern-AI](https:\u002F\u002Fengineering.nyu.edu\u002Facademics\u002Fdepartments\u002Felectrical-and-computer-engineering\u002Fece-seminar-series\u002Fmodern-artificial) | [YouTube讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLhwo5ntex8iY9xhpSwWas451NgVuqBE7U) | 2017至今 |\n| 4. | **深度学习：炼金术还是科学？** | 众多传奇人物，普林斯顿高等研究院 | [DLAS](https:\u002F\u002Fvideo.ias.edu\u002Fdeeplearning\u002F2019\u002F0222) \u003Cbr\u002F> [议程](https:\u002F\u002Fwww.math.ias.edu\u002Ftml\u002Fdlasagenda) | [YouTube讲座](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLdDZb3TwJPZ7aAxhIHALBoh8l6-UxmMNP) | 2019 |\n| | | | | | |\n\n[前往目录 :arrow_heading_up:](https:\u002F\u002Fgithub.com\u002Fkmario23\u002Fdeep-learning-drizzle#contents)\n\n:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:\n\n### 待办事项 :running:\n:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:\n\n:white_large_square: 构建机器学习、深度学习和强化学习基础的优化课程\n\n:white_large_square: 以深度学习和机器学习为主的计算机视觉课程\n\n:white_large_square: 以深度学习为主的语音识别课程\n\n:white_large_square: 关于几何与图神经网络的结构化课程\n\n:white_large_square: 自动驾驶汽车专题\n\n:white_large_square: 以机器学习\u002F深度学习为重点的计算机图形学专题\n\n:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:\n\n[前往目录 :arrow_heading_up:](https:\u002F\u002Fgithub.com\u002Fkmario23\u002Fdeep-learning-drizzle#contents) \n\n:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:\n\n\n### 网络精选 :earth_asia:\n\n - [Montreal.AI](http:\u002F\u002Fwww.montreal.ai\u002Fai4all.pdf)\n - [UPC-DLAI-2018](https:\u002F\u002Ftelecombcn-dl.github.io\u002F2018-dlai\u002F)\n - [UPC-DLAI-2019](https:\u002F\u002Ftelecombcn-dl.github.io\u002Fdlai-2019\u002F)\n - [www.hashtagtechgeek.com](https:\u002F\u002Fwww.hashtagtechgeek.com\u002F2019\u002F10\u002F250-machine-learning-deep-learning-videos-courseware.html)\n - [UPC-Barcelona, IDL-2020](https:\u002F\u002Ftelecombcn-dl.github.io\u002Fidl-2020\u002F) \n - [UPC-DLAI-2020](https:\u002F\u002Ftelecombcn-dl.github.io\u002Fdlai-2020) \n\n:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:\n\n\n### 贡献 :pray:\n\n如果您发现任何符合上述类别（即深度学习、机器学习、强化学习、计算机视觉、自然语言处理）的课程，**并且**该课程提供讲座视频（幻灯片为可选），请按照上述格式更新课程信息，并提交问题或拉取请求。\n\n\n:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:\n\n\n### 支持 :moneybag:\n\n**可选：** 如果您是一位善良的善心人士，愿意支持我，请在力所能及的情况下给予帮助，我将永远感激不尽。更重要的是，您的支持会让我在困难时期更有动力继续工作 :pray:\n\n[![](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fkmario23_deep-learning-drizzle_readme_3ed0eaf21fb2.gif)](https:\u002F\u002Fwww.paypal.com\u002Fcgi-bin\u002Fwebscr?cmd=_s-xclick&hosted_button_id=NT3EATS5N35WU)\n\n\n非常感谢！ :blue_heart: \n\n:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:\n\n### :礼物心形: :毕业帽: :毕业帽: :毕业帽: ……（重复25次） :礼物心形: \n\n:大减号::大减号::大减号::……（重复30次） :大减号::大减号::大减号:","# deep-learning-drizzle 快速上手指南\n\n**项目说明**：`deep-learning-drizzle` 并非一个需要安装运行的软件库或框架，而是一个**精选的深度学习教育资源索引仓库**。它汇集了全球顶尖大学（如斯坦福、多伦多大学、牛津大学等）的深度学习、机器学习、自然语言处理及计算机视觉等领域的课程视频、讲义和代码链接。\n\n本指南将指导你如何获取并利用这些资源进行学习。\n\n## 1. 环境准备\n\n由于本项目本质是资源列表，无需特定的运行时环境（如 Python 版本或 GPU 驱动），但为了顺利学习课程中的实战代码，建议准备以下基础环境：\n\n* **操作系统**：Windows \u002F macOS \u002F Linux 均可\n* **网络环境**：\n * 大部分视频托管于 YouTube，国内访问可能需要网络代理工具。\n * 部分课程代码托管于 GitHub，建议配置好 Git 环境。\n* **前置依赖（针对课程实战）**：\n * **Python**: 建议安装 Python 3.8+\n * **包管理**: `pip` 或 `conda`\n * **核心库**: 根据具体课程需求安装 `numpy`, `pandas`, `matplotlib`, `pytorch` 或 `tensorflow`。\n * **国内加速**: 推荐使用清华源或阿里源安装 Python 依赖：\n ```bash\n pip install -i https:\u002F\u002Fpypi.tuna.tsinghua.edu.cn\u002Fsimple \u003Cpackage_name>\n ```\n\n## 2. 获取资源步骤\n\n你可以通过克隆仓库或直接在浏览器中查看的方式获取资源列表。\n\n### 方式一：在线浏览（推荐）\n直接访问 GitHub 仓库页面查看整理好的课程表格：\n[https:\u002F\u002Fgithub.com\u002Fkmario23\u002Fdeep-learning-drizzle](https:\u002F\u002Fgithub.com\u002Fkmario23\u002Fdeep-learning-drizzle)\n\n### 方式二：克隆到本地\n如果你希望离线查看或贡献内容，可以使用以下命令克隆仓库：\n\n```bash\ngit clone https:\u002F\u002Fgithub.com\u002Fkmario23\u002Fdeep-learning-drizzle.git\ncd deep-learning-drizzle\n```\n\n*注：若 GitHub 访问缓慢，可使用国内镜像加速（如通过 proxy 或 gitee 镜像，若有）：*\n```bash\n# 示例：使用 git 深度克隆（仅获取最新内容，节省时间）\ngit clone --depth 1 https:\u002F\u002Fgithub.com\u002Fkmario23\u002Fdeep-learning-drizzle.git\n```\n\n## 3. 基本使用\n\n本项目的“使用”即指**选择课程并开始学习**。仓库 `README` 中已将资源分类为多个领域，请根据你的学习目标选择对应的章节。\n\n### 使用示例：入门深度学习\n\n假设你想从零开始学习深度学习基础，可以按照以下步骤操作：\n\n1. **定位课程**：\n 在仓库页面的 **\"Deep Learning (Deep Neural Networks)\"** 板块中，找到排名第一的课程：\n * **课程名称**: Neural Networks for Machine Learning\n * **讲师**: Geoffrey Hinton (多伦多大学)\n * **年份**: 2012\u002F2014\n\n2. **获取学习资料**：\n * **观看视频**: 点击表格中的 `YouTube-Lectures` 链接（需网络支持）。\n * **下载课件**: 点击 `Lecture-Slides` 链接获取 PPT。\n * **获取代码**: 部分课程（如 \"Neural Networks Demystified\"）提供配套代码，点击 `Suppl. Code` 链接跳转至 GitHub 仓库并克隆：\n ```bash\n git clone https:\u002F\u002Fgithub.com\u002Fstephencwelch\u002FNeural-Networks-Demystified.git\n cd Neural-Networks-Demystified\n ```\n\n3. **运行示例代码**（以 Welch Labs 课程为例）：\n 进入代码目录后，通常可以直接运行 Python 脚本体验：\n ```bash\n # 安装依赖 (如有 requirements.txt)\n pip install -r requirements.txt\n \n # 运行第一个示例\n python part1.py\n ```\n\n### 其他热门领域指引\n\n* **计算机视觉**: 查找 **\"Modern Computer Vision\"** 或 **\"Deep Learning (Deep Neural Networks)\"** 下的 `CS231n` 课程（Stanford）。\n* **自然语言处理**: 查找 **\"Natural Language Processing\"** 下的 `CS224n` 课程（Stanford）。\n* **强化学习**: 直接跳转至 **\"Reinforcement Learning\"** 板块选择对应训练营课程。\n\n> **提示**：Geoffrey Hinton 教授的名言是本项目的核心理念：“学得足够多以便建立直觉，然后相信你的直觉并勇往直前！” (Read enough so you start developing intuitions and then trust your intuitions and go for it!)","某初创公司的算法工程师小李正试图从零构建一个能够识别医疗影像中病变区域的深度学习模型，但他面对庞杂的数学公式和分散的网络教程感到无从下手。\n\n### 没有 deep-learning-drizzle 时\n- **知识碎片化严重**：需要在 Coursera、YouTube 和个人博客间反复跳转，难以将深度学习基础、优化算法与计算机视觉知识串联成完整体系。\n- **直觉建立缓慢**：缺乏像 Geoffrey Hinton 等顶尖专家的系统性讲座指引，只能死记硬背代码实现，无法理解模型背后的概率图模型原理，导致调参全靠猜。\n- **领域覆盖不全**：在专注于图像识别时，忽略了自然语言处理或语音识别中的通用特征提取技巧，错失了跨领域迁移学习的机会，浪费了数周时间重复造轮子。\n- **学习路径迷茫**：面对海量资源无法判断优先级，常常陷入“收藏从未停止，学习从未开始”的困境，项目启动周期被无限拉长。\n\n### 使用 deep-learning-drizzle 后\n- **课程体系系统化**：直接利用该工具整理的结构化清单，按顺序攻克从机器学习基础到现代计算机视觉的核心课程，知识脉络清晰可见。\n- **专家直觉内化**：通过研读收录的名师讲座，快速建立起对神经网络行为的直观理解，能够依据理论直觉大胆调整架构，显著减少了盲目试错。\n- **跨域能力融合**：借助其涵盖的 NLP、语音识别及强化学习等多板块内容，成功将其他领域的先进预处理方法迁移至医疗影像任务，提升了模型泛化能力。\n- **研发效率倍增**：不再浪费时间在搜索资源上，而是专注于代码落地与实验验证，将原本需要一个月的技术预研期压缩至一周内完成。\n\ndeep-learning-drizzle 的核心价值在于它将散落的顶级学术资源转化为一条清晰的成长路径，让开发者能从“盲目模仿代码”进阶为“依靠直觉创新”。","https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fkmario23_deep-learning-drizzle_d70dbdba.png","kmario23","Mario","https:\u002F\u002Foss.gittoolsai.com\u002Favatars\u002Fkmario23_a93e14a8.jpg","Pixel processor 📸 Neural Networks 🧠 Python fanatic 🐍 JAX & NumPy ninja assuaging accelerators ⚡ Sedulous solo traveller 🌴","Universität Stuttgart","Stuttgart, DE",null,"ScientificML","https:\u002F\u002Ftinyurl.com\u002Fkmario23","https:\u002F\u002Fgithub.com\u002Fkmario23",[83,87],{"name":84,"color":85,"percentage":86},"HTML","#e34c26",87.3,{"name":88,"color":89,"percentage":90},"Python","#3572A5",12.7,12798,2971,"2026-04-06T05:43:23",1,"",{"notes":97,"python":95,"dependencies":98},"该项目并非可运行的软件工具，而是一个深度学习课程、讲座和资源的学习清单（Awesome List）。README 内容主要列出了来自多伦多大学、斯坦福大学等机构的公开课链接（如 CS231n, CS224d 等），因此不存在特定的操作系统、GPU、内存、Python 版本或依赖库的安装需求。用户只需通过浏览器访问提供的链接即可学习相关内容。",[],[35,100,14,15],"音频",[102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121],"machine-learning","deep-learning","deep-neural-networks","pattern-recognition","computer-vision","optimization","visual-recognition","reinforcement-learning","deep-reinforcement-learning","natural-language-processing","artificial-neural-networks","artificial-intelligence-algorithms","probabilistic-graphical-models","bayesian-statistics","speech-recognition","graph-neural-networks","medical-imaging","geometric-deep-learning","explainable-ai","probability","2026-03-27T02:49:30.150509","2026-04-08T10:02:36.527809",[],[126],{"id":127,"version":128,"summary_zh":129,"released_at":130},145673,"v19.11-lw","由于清单已基本稳定，我开始创建发布版本，以便更清晰地展示自上次提交以来有哪些变化。我计划按照以下格式命名发布版本：`v` 表示版本号，接下来的两位数字表示当前年份（例如 `19` 代表 2019 年），最后两位数字则表示该年的月份（例如 `11` 代表 11 月）。\n\n本次发布的具体内容如下（[`v19.11`](https:\u002F\u002Fgithub.com\u002Fkmario23\u002Fdeep-learning-drizzle\u002Freleases\u002Ftag\u002Fv19.11-lw)）：\n\n- 修复了一些失效的 URL\n- 在机器学习基础部分新增了一门课程\n- 在优化方法部分新增了一门课程\n- 在训练营板块添加了一些优质且近期的演讲视频","2019-11-13T14:45:58"]