{"id":13415971,"url":"https://github.com/kmario23/deep-learning-drizzle","last_synced_at":"2025-03-25T13:44:17.222Z","repository":{"id":37444315,"uuid":"159091924","full_name":"kmario23/deep-learning-drizzle","owner":"kmario23","description":"Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!","archived":false,"fork":false,"pushed_at":"2024-10-19T17:28:52.000Z","size":267,"stargazers_count":12449,"open_issues_count":5,"forks_count":2940,"subscribers_count":609,"default_branch":"master","last_synced_at":"2025-01-30T12:46:45.383Z","etag":null,"topics":["artificial-intelligence-algorithms","artificial-neural-networks","bayesian-statistics","computer-vision","deep-learning","deep-neural-networks","deep-reinforcement-learning","explainable-ai","geometric-deep-learning","graph-neural-networks","machine-learning","medical-imaging","natural-language-processing","optimization","pattern-recognition","probabilistic-graphical-models","probability","reinforcement-learning","speech-recognition","visual-recognition"],"latest_commit_sha":null,"homepage":"https://deep-learning-drizzle.github.io","language":"HTML","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/kmario23.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2018-11-26T01:17:36.000Z","updated_at":"2025-01-30T02:43:29.000Z","dependencies_parsed_at":"2025-01-30T12:41:35.471Z","dependency_job_id":"d2c1083b-607a-41ab-b29e-476611ff7e0d","html_url":"https://github.com/kmario23/deep-learning-drizzle","commit_stats":null,"previous_names":[],"tags_count":1,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kmario23%2Fdeep-learning-drizzle","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kmario23%2Fdeep-learning-drizzle/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kmario23%2Fdeep-learning-drizzle/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kmario23%2Fdeep-learning-drizzle/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/kmario23","download_url":"https://codeload.github.com/kmario23/deep-learning-drizzle/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":245474569,"owners_count":20621430,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["artificial-intelligence-algorithms","artificial-neural-networks","bayesian-statistics","computer-vision","deep-learning","deep-neural-networks","deep-reinforcement-learning","explainable-ai","geometric-deep-learning","graph-neural-networks","machine-learning","medical-imaging","natural-language-processing","optimization","pattern-recognition","probabilistic-graphical-models","probability","reinforcement-learning","speech-recognition","visual-recognition"],"created_at":"2024-07-30T21:00:53.369Z","updated_at":"2025-03-25T13:44:17.162Z","avatar_url":"https://github.com/kmario23.png","language":"HTML","funding_links":[],"categories":["Others","HTML","A01_机器学习教程","100 + 𝗔𝗿𝘁𝗶𝗳𝗶𝗰𝗶𝗮𝗹 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗟𝗶𝘀𝘁 𝘄𝗶𝘁𝗵 𝗰𝗼𝗱𝗲","Online courses","Deep Learning","Courses or Paper","📚 Project Purpose","Image Generation \u0026 Editing"],"sub_categories":["Knowledge Graphs","2. Documentation","Machine Learning (Entry-Level)"],"readme":"# :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://www.deeplearning.ai/hodl-geoffrey-hinton/) :books: ​\u003cbr/\u003e 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://github.com/kmario23/deep-learning-drizzle#tada-deep-learning-deep-neural-networks-confetti_ball-balloon) | **Probabilistic Graphical Models** [:arrow_heading_down:](https://github.com/kmario23/deep-learning-drizzle#loudspeaker-probabilistic-graphical-models-sparkles) |\n| | |\n| **Machine Learning Fundamentals** [:arrow_heading_down:](https://github.com/kmario23/deep-learning-drizzle#cupid-machine-learning-fundamentals-cyclone-boom) | **Natural Language Processing** [:arrow_heading_down:](https://github.com/kmario23/deep-learning-drizzle#hibiscus-natural-language-processing-cherry_blossom-sparkling_heart) |\n| | |\n| **Optimization for Machine Learning** [:arrow_heading_down:](https://github.com/kmario23/deep-learning-drizzle#cupid-optimization-for-machine-learning-cyclone-boom) | **Automatic Speech Recognition** [:arrow_heading_down:](https://github.com/kmario23/deep-learning-drizzle#speaking_head-automatic-speech-recognition-speech_balloon-thought_balloon) |\n| | |\n| **General Machine Learning** [:arrow_heading_down:](https://github.com/kmario23/deep-learning-drizzle#cupid-general-machine-learning-cyclone-boom) | **Modern Computer Vision** [:arrow_heading_down:](https://github.com/kmario23/deep-learning-drizzle#fire-modern-computer-vision-camera_flash-movie_camera) |\n| | |\n| **Reinforcement Learning** [:arrow_heading_down:](https://github.com/kmario23/deep-learning-drizzle#balloon-reinforcement-learning-hotsprings-video_game) | **Boot Camps or Summer Schools** [:arrow_heading_down:](https://github.com/kmario23/deep-learning-drizzle#star2-boot-camps-or-summer-schools-maple_leaf) |\n| | |\n| **Bayesian Deep Learning** [:arrow_heading_down:](https://github.com/kmario23/deep-learning-drizzle#game_die-bayesian-deep-learning-spades-gem) | **Medical Imaging** [:arrow_heading_down:](https://github.com/kmario23/deep-learning-drizzle#movie_camera-medical-imaging-camera-video_camera) |\n| | |\n| **Graph Neural Networks** [:arrow_heading_down: ](https://github.com/kmario23/deep-learning-drizzle#tada-graph-neural-networks-geometric-dl-confetti_ball-balloon) | **Bird's-eye view of Artificial Intelligence** [:arrow_heading_down:](https://github.com/kmario23/deep-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/Instructor(s) | Course WebPage | Lecture Videos | Year |\n| ---- | ----------------------------------------------------- | ---------------------------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | --------------- |\n| 1. | **Neural Networks for Machine Learning** | Geoffrey Hinton, University of Toronto | [Lecture-Slides](http://www.cs.toronto.edu/~hinton/coursera_slides.html) \u003cbr/\u003e [CSC321-tijmen](https://www.cs.toronto.edu/~tijmen/csc321/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLoRl3Ht4JOcdU872GhiYWf6jwrk_SNhz9) \u003cbr/\u003e [UofT-mirror](https://www.cs.toronto.edu/~hinton/coursera_lectures.html) | 2012 \u003cbr/\u003e 2014 |\n| 2. | **Neural Networks Demystified** | Stephen Welch, Welch Labs | [Suppl. Code](https://github.com/stephencwelch/Neural-Networks-Demystified) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLiaHhY2iBX9hdHaRr6b7XevZtgZRa1PoU) | 2014 |\n| 3. | **Deep Learning at Oxford** | Nando de Freitas, Oxford University | [Oxford-ML](http://www.cs.ox.ac.uk/teaching/courses/2014-2015/ml/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLE6Wd9FR--EfW8dtjAuPoTuPcqmOV53Fu) | 2015 |\n| 4. | **Deep Learning for Perception** | Dhruv Batra, Virginia Tech | [ECE-6504](https://computing.ece.vt.edu/~f15ece6504/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL-fZD610i7yAsfH2eLBiRDa90kL2ML0f7) | 2015 |\n| 5. | **Deep Learning** | Ali Ghodsi, University of Waterloo | [STAT-946](https://uwaterloo.ca/data-analytics/deep-learning) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLehuLRPyt1Hyi78UOkMPWCGRxGcA9NVOE) | F2015 |\n| 6. | **CS231n: CNNs for Visual Recognition** | Andrej Karpathy, Stanford University | [CS231n](http://cs231n.stanford.edu/2015/) | `None` | 2015 |\n| 7. | **CS224d: Deep Learning for NLP** | Richard Socher, Stanford University | [CS224d](http://cs224d.stanford.edu) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLmImxx8Char8dxWB9LRqdpCTmewaml96q) | 2015 |\n| 8. | **Bay Area Deep Learning** | Many legends, Stanford | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLrAXtmErZgOfMuxkACrYnD2fTgbzk2THW) | 2016 |\n| 9. | **CS231n: CNNs for Visual Recognition** | Andrej Karpathy, Stanford University | [CS231n](http://cs231n.stanford.edu/2016/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLkt2uSq6rBVctENoVBg1TpCC7OQi31AlC) \u003cbr/\u003e[(Academic Torrent)](https://academictorrents.com/details/46c5af9e2075d9af06f280b55b65cf9b44eb9fe7) | 2016 |\n| 10. | **Neural Networks** | Hugo Larochelle, Université de Sherbrooke | [Neural-Networks](http://info.usherbrooke.ca/hlarochelle/neural_networks/content.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL6Xpj9I5qXYEcOhn7TqghAJ6NAPrNmUBH) \u003cbr/\u003e [(Academic Torrent)](https://academictorrents.com/details/e046bca3bc837053d1609ef33d623ee5c5af7300) | 2016 |\n| | | | | | |\n| 11. | **CS224d: Deep Learning for NLP** | Richard Socher, Stanford University | [CS224d](http://cs224d.stanford.edu) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLlJy-eBtNFt4CSVWYqscHDdP58M3zFHIG) \u003cbr/\u003e[(Academic Torrent)](https://academictorrents.com/details/dd9b74b50a1292b4b154094b7338ec1d66e8894d) | 2016 |\n| 12. | **CS224n: NLP with Deep Learning** | Richard Socher, Stanford University | [CS224n](http://web.stanford.edu/class/cs224n/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL3FW7Lu3i5Jsnh1rnUwq_TcylNr7EkRe6) | 2017 |\n| 13. | **CS231n: CNNs for Visual Recognition** | Justin Johnson, Stanford University | [CS231n](http://cs231n.stanford.edu/2017/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL3FW7Lu3i5JvHM8ljYj-zLfQRF3EO8sYv) \u003cbr/\u003e [(Academic Torrent)](https://academictorrents.com/details/ed8a16ebb346e14119a03371665306609e485f13) | 2017 |\n| 14. | **Topics in Deep Learning** | Ruslan Salakhutdinov, CMU | [10707](https://deeplearning-cmu-10707.github.io/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLpIxOj-HnDsOSL__Buy7_UEVQkyfhHapa) | F2017 |\n| 15. | **Deep Learning Crash Course** | Leo Isikdogan, UT Austin | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLWKotBjTDoLj3rXBL-nEIPRN9V3a9Cx07) | 2017 |\n| 16. | **Deep Learning and its Applications** | François Pitié, Trinity College Dublin | [EE4C16](https://github.com/frcs/4C16-2017) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLIo1iEzl5iB9NkulNR0X5vXN8AaEKglWT) | 2017 |\n| 17. | **Deep Learning** | Andrew Ng, Stanford University | [CS230](http://cs230.stanford.edu/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLoROMvodv4rOABXSygHTsbvUz4G_YQhOb) | 2018 |\n| 18. | **UvA Deep Learning** | Efstratios Gavves, University of Amsterdam | [UvA-DLC](https://uvadlc.github.io/) | [Lecture-Videos](https://uvadlc.github.io/lectures-sep2018.html) | 2018 |\n| 19. | **Advanced Deep Learning and Reinforcement Learning** | Many legends, DeepMind | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLqYmG7hTraZDNJre23vqCGIVpfZ_K2RZs) | 2018 |\n| 20. | **Machine Learning** | Peter Bloem, Vrije Universiteit Amsterdam | [MLVU](https://mlvu.github.io/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLCof9EqayQgsORO3pFzeYZFz6cszYO0VJ) | 2018 |\n| | | | | | |\n| 21. | **Deep Learning** | Francois Fleuret, EPFL | [EE-59](https://fleuret.org/ee559-2018/dlc) | [Video-Lectures](https://fleuret.org/ee559-2018/dlc/#materials) | 2018 |\n| 22. | **Introduction to Deep Learning** | Alexander Amini, Harini Suresh and others, MIT | [6.S191](http://introtodeeplearning.com/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLtBw6njQRU-rwp5__7C0oIVt26ZgjG9NI) \u003cbr/\u003e [2017-version](https://www.youtube.com/playlist?list=PLkkuNyzb8LmxFutYuPA7B4oiMn6cjD6Rs) | 2017- 2021 |\n| 23. | **Deep Learning for Self-Driving Cars** | Lex Fridman, MIT | [6.S094](https://selfdrivingcars.mit.edu/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLrAXtmErZgOeiKm4sgNOknGvNjby9efdf) | 2017-2018 |\n| 24. | **Introduction to Deep Learning** | Bhiksha Raj and many others, CMU | [11-485/785](http://deeplearning.cs.cmu.edu/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLp-0K3kfddPwJBJ4Q8We-0yNQEG0fZrSa) | S2018 |\n| 25. | **Introduction to Deep Learning** | Bhiksha Raj and many others, CMU | [11-485/785](http://deeplearning.cs.cmu.edu/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLp-0K3kfddPyH44FP0dl0CbYprvTcfgOI) [Recitation-Inclusive](https://www.youtube.com/playlist?list=PLLR0_ZOlbfD6KDBq93G8-guHI-J1ICeFm) | F2018 |\n| 26. | **Deep Learning Specialization** | Andrew Ng, Stanford | [DL.AI](https://www.deeplearning.ai/deep-learning-specialization/) | [YouTube-Lectures](https://www.youtube.com/channel/UCcIXc5mJsHVYTZR1maL5l9w/playlists) | 2017-2018 |\n| 27. | **Deep Learning** | Ali Ghodsi, University of Waterloo | [STAT-946](https://uwaterloo.ca/data-analytics/teaching/deep-learning-2017) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLehuLRPyt1HxTolYUWeyyIoxDabDmaOSB) | F2017 |\n| 28. | **Deep Learning** | Mitesh Khapra, IIT-Madras | [CS7015](https://www.cse.iitm.ac.in/~miteshk/CS7015.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLyqSpQzTE6M9gCgajvQbc68Hk_JKGBAYT) | 2018 |\n| 29. | **Deep Learning for AI** | UPC Barcelona | [DLAI-2017](https://telecombcn-dl.github.io/2017-dlai/) \u003cbr/\u003e [DLAI-2018](https://telecombcn-dl.github.io/2018-dlai/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL-5eMc3HQTBagIUjKefjcTbnXC0wXC_vd) | 2017-2018 |\n| 30. | **Deep Learning** | Alex Bronstein and Avi Mendelson, Technion | [CS236605](https://vistalab-technion.github.io/cs236605/info/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLM0a6Z788YAZuqg2Ip-_dPLzEd33lZvP2) | 2018 |\n| | | | | | |\n| 31. | **MIT Deep Learning** | Many Researchers, Lex Fridman, MIT | [6.S094, 6.S091, 6.S093](https://deeplearning.mit.edu/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLrAXtmErZgOeiKm4sgNOknGvNjby9efdf) | 2019 |\n| 32. | **Deep Learning Book** companion videos | Ian Goodfellow and others | [DL-book slides](https://www.deeplearningbook.org/lecture_slides.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLsXu9MHQGs8df5A4PzQGw-kfviylC-R9b) | 2017 |\n| 33. | **Theories of Deep Learning** | Many Legends, Stanford | [Stats-385](https://stats385.github.io/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLwUqqMt5en7fFLwSDa9V3JIkDam-WWgqy) \u003cbr/\u003e (first 10 lectures) | F2017 |\n| 34. | **Neural Networks** | Grant Sanderson | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi) | 2017-2018 |\n| 35. | **CS230: Deep Learning** | Andrew Ng, Kian Katanforoosh, Stanford | [CS230](http://cs230.stanford.edu/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLoROMvodv4rOABXSygHTsbvUz4G_YQhOb) | A2018 |\n| 36. | **Theory of Deep Learning** | Lots of Legends, Canary Islands | [DALI'18](http://dalimeeting.org/dali2018/workshopTheoryDL.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLeCNfJWZKqxtWBnV8gefGqmmPgz9YF4LR) | 2018 |\n| 37. | **Introduction to Deep Learning** | Alex Smola, UC Berkeley | [Stat-157](http://courses.d2l.ai/berkeley-stat-157/index.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLZSO_6-bSqHQHBCoGaObUljoXAyyqhpFW) | S2019 |\n| 38. | **Deep Unsupervised Learning** | Pieter Abbeel, UC Berkeley | [CS294-158](https://sites.google.com/view/berkeley-cs294-158-sp19/home) | [YouTube-Lectures](https://www.youtube.com/channel/UCf4SX8kAZM_oGcZjMREsU9w/videos) | S2019 |\n| 39. | **Machine Learning** | Peter Bloem, Vrije Universiteit Amsterdam | [MLVU](https://mlvu.github.io/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLCof9EqayQgupldnTvqNy_BThTcME5r93) | 2019 |\n| 40. | **Deep Learning on Computational Accelerators** | Alex Bronstein and Avi Mendelson, Technion | [CS236605](https://vistalab-technion.github.io/cs236605/lectures/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLM0a6Z788YAa_WCy_V-q9NrGm5qQegZR5) | S2019 |\n| | | | | | |\n| 41. | **Introduction to Deep Learning** | Bhiksha Raj and many others, CMU | [11-785](http://www.cs.cmu.edu/~bhiksha/courses/deeplearning/Spring.2019/www) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLp-0K3kfddPzNdZPX4p0lVi6AcDXBofuf) | S2019 |\n| 42. | **Introduction to Deep Learning** | Bhiksha Raj and many others, CMU | [11-785](https://www.cs.cmu.edu/~bhiksha/courses/deeplearning/Fall.2019/www) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLp-0K3kfddPwz13VqV1PaMXF6V6dYdEsj) \u003cbr\u003e [Recitations](https://www.youtube.com/playlist?list=PLp-0K3kfddPxf4T59JEQKv5UanLPVsxzz) | F2019 |\n| 43. | **UvA Deep Learning** | Efstratios Gavves, University of Amsterdam | [UvA-DLC](https://uvadlc.github.io/) | [Lecture-Videos](https://uvadlc.github.io/lectures-apr2019.html) | S2019 |\n| 44. | **Deep Learning** | Prabir Kumar Biswas, IIT Kgp | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLbRMhDVUMngc7NM-gDwcBzIYZNFSK2N1a) | 2019 |\n| 45. | **Deep Learning and its Applications** | Aditya Nigam, IIT Mandi | [CS-671](http://faculty.iitmandi.ac.in/~aditya/cs671/index.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLKvX2d3IUq586Ic9gIhZj6ubpWV-OJfl4) | 2019 |\n| 46. | **Neural Networks** | Neil Rhodes, Harvey Mudd College | [CS-152](https://www.cs.hmc.edu/~rhodes/cs152/schedule.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLgEuVSRbAI9UIQSHGy4l01laA_12YOqEj) | F2019 |\n| 47. | **Deep Learning** | Thomas Hofmann, ETH Zürich | [DAL-DL](http://www.da.inf.ethz.ch/teaching/2019/DeepLearning) | [Lecture-Videos](https://video.ethz.ch/lectures/d-infk/2019/autumn/263-3210-00L.html) | F2019 |\n| 48. | **Deep Learning** | Milan Straka, Charles University | [NPFL114](https://ufal.mff.cuni.cz/courses/npfl114) | [Lecture-Videos](https://ufal.mff.cuni.cz/courses/npfl114/1718-summer) | S2019 |\n| 49. | **UvA Deep Learning** | Efstratios Gavves, University of Amsterdam | [UvA-DLC-19](https://uvadlc.github.io/#lectures) | [Lecture-Videos](https://uvadlc.github.io/#lectures) | F2019 |\n| 50. | **Artificial Intelligence: Principles and Techniques** | Percy Liang and Dorsa Sadigh, Stanford University | [CS221](https://stanford-cs221.github.io/autumn2019/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLoROMvodv4rO1NB9TD4iUZ3qghGEGtqNX) | F2019 |\n| | | | | | |\n| 51. | **Analyses of Deep Learning** | Lots of Legends, Stanford University | [STATS-385](https://stats385.github.io/) | [YouTube-Lectures](https://stats385.github.io/lecture_videos) | 2017-2019 |\n| 52. | **Deep Learning Foundations and Applications** | Debdoot Sheet and Sudeshna Sarkar, IIT-Kgp | [AI61002](http://www.facweb.iitkgp.ac.in/~debdoot/courses/AI61002/Spr2020) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL_AdDfjIMo6pZfwjZ0rJlkE_MIsmRW7Mh) | S2020 |\n| 53. | **Designing, Visualizing, and Understanding Deep Neural Networks** | John Canny, UC Berkeley | [CS 182/282A](https://bcourses.berkeley.edu/courses/1487769/pages/cs-l-w-182-slash-282a-designing-visualizing-and-understanding-deep-neural-networks-spring-2020) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLkFD6_40KJIwaO6Eca8kzsEFBob0nFvwm) | S2020 |\n| 54. | **Deep Learning** | Yann LeCun and Alfredo Canziani, NYU | [DS-GA 1008](https://atcold.github.io/pytorch-Deep-Learning/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLLHTzKZzVU9eaEyErdV26ikyolxOsz6mq) | S2020 |\n| 55. | **Introduction to Deep Learning** | Bhiksha Raj, CMU | [11-785](https://deeplearning.cs.cmu.edu/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLp-0K3kfddPzCnS4CqKphh-zT3aDwybDe) | S2020 |\n| 56. | **Deep Unsupervised Learning** | Pieter Abbeel, UC Berkeley | [CS294-158](https://sites.google.com/view/berkeley-cs294-158-sp20) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLwRJQ4m4UJjPiJP3691u-qWwPGVKzSlNP) | S2020 |\n| 57. | **Machine Learning** | Peter Bloem, Vrije Universiteit Amsterdam | [VUML](https://mlvu.github.io/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLCof9EqayQgthR7IViXkAkUwel_rhxGYM) | S2020 |\n| 58. | **Deep Learning (with PyTorch)** | Alfredo Canziani and Yann LeCun, NYU | [DS-GA 1008](https://atcold.github.io/pytorch-Deep-Learning/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLLHTzKZzVU9eaEyErdV26ikyolxOsz6mq) | S2020 |\n| 59. | **Introduction to Deep Learning and Generative Models** | Sebastian Raschka, UW-Madison | [Stat453](http://pages.stat.wisc.edu/~sraschka/teaching/stat453-ss2020/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLTKMiZHVd_2JkR6QtQEnml7swCnFBtq4P) | S2020 |\n| 60. | **Deep Learning** | Andreas Maier, FAU Erlangen-Nürnberg | [DL-2020](https://www.video.uni-erlangen.de/course/id/925) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLpOGQvPCDQzvgpD3S0vTy7bJe2pf_yJFj) \u003cbr/\u003e[Lecture-Videos](https://www.video.uni-erlangen.de/course/id/925) | SS2020 |\n| | | | | | |\n| 61. | **Introduction to Deep Learning** | Laura Leal-Taixé and Matthias Niessner, TU-München | [I2DL-IN2346](https://dvl.in.tum.de/teaching/i2dl-ss20/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLQ8Y4kIIbzy_OaXv86lfbQwPHSomk2o2e) | SS2020 |\n| 62. | **Deep Learning** | Sargur Srihari, SUNY-Buffalo | [CSE676](https://cedar.buffalo.edu/~srihari/CSE676/) | [YouTube-Lectures-P1](https://www.youtube.com/playlist?list=PLmx4utxjUQD70k_NzeiSIXf30m54T_e1h) \u003cbr/\u003e[YouTube-Lectures-P2](https://www.youtube.com/channel/UCUm7yUmVJyAbYh_0ppJ4H-g/videos) | 2020 |\n| 63. | **Deep Learning Lecture Series** | Lots of Legends, DeepMind x UCL, London | [DLLS-20](https://deepmind.com/learning-resources/deep-learning-lecture-series-2020) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLqYmG7hTraZCDxZ44o4p3N5Anz3lLRVZF) | 2020 |\n| 64. | **MultiModal Machine Learning** | Louis-Philippe Morency \u0026 others, Carnegie Mellon University | [11-777 MMML-20](https://cmu-multicomp-lab.github.io/mmml-course/fall2020) | [YouTube-Lectures](https://www.youtube.com/channel/UCqlHIJTGYhiwQpNuPU5e2gg/videos) | F2020 |\n| 65. | **Reliable and Interpretable Artificial Intelligence** | Martin Vechev, ETH Zürich | [RIAI-20](https://www.sri.inf.ethz.ch/teaching/riai2020) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLWjm4hHpaNg6c-W7JjNYDEC_kJK9oSp0Y) | F2020 |\n| 66. | **Fundamentals of Deep Learning** | David McAllester, Toyota Technological Institute, Chicago | [TTIC-31230](https://mcallester.github.io/ttic-31230/Fall2020) | [YouTube-Lectures](https://www.youtube.com/channel/UCciVrtrRR3bQdaGbti9-hVQ/videos) | F2020 |\n| 67. | **Foundations of Deep Learning** | Soheil Feize, University of Maryland, College Park | [CMSC 828W](http://www.cs.umd.edu/class/fall2020/cmsc828W) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLHgjs9ncvHi80UCSlSvQe-TK_uOyDv_Jf) | F2020 |\n| 68. | **Deep Learning** | Andreas Geiger, Universität Tübingen | [DL-UT](https://uni-tuebingen.de/fakultaeten/mathematisch-naturwissenschaftliche-fakultaet/fachbereiche/informatik/lehrstuehle/autonomous-vision/teaching/lecture-deep-learning/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL05umP7R6ij3NTWIdtMbfvX7Z-4WEXRqD) | W20/21 |\n| 69. | **Deep Learning** | Andreas Maier, FAU Erlangen-Nürnberg | [DL-FAU](https://www.fau.tv/course/id/1599) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLpOGQvPCDQzvJEPFUQ3mJz72GJ95jyZTh) | W20/21 |\n| 70. | **Fundamentals of Deep Learning** | Terence Parr and Yannet Interian, University of San Francisco | [DL-Fundamentals](https://github.com/parrt/fundamentals-of-deep-learning) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLFCc_Fc116ikeol9CZcWWKqmrJljxhE4N) | S2021 |\n| | | | | | |\n| 71. | **Full Stack Deep Learning** | Pieter Abbeel, Sergey Karayev, UC Berkeley | [FS-DL](https://fullstackdeeplearning.com/spring2021) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL1T8fO7ArWlcWg04OgNiJy91PywMKT2lv) | S2021 |\n| 72. | **Deep Learning: Designing, Visualizing, and Understanding DNNs** | Sergey Levine, UC Berkeley | [CS 182](https://cs182sp21.github.io) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL_iWQOsE6TfVmKkQHucjPAoRtIJYt8a5A) | S2021 |\n| 73. | **Deep Learning in the Life Sciences** | Manolis Kellis, MIT | [6.874](https://mit6874.github.io) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLypiXJdtIca5sxV7aE3-PS9fYX3vUdIOX) | S2021 |\n| 74. | **Introduction to Deep Learning and Generative Models** | Sebastian Raschka, University of Wisconsin-Madison | [Stat 453](http://pages.stat.wisc.edu/~sraschka/teaching/stat453-ss2021) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLTKMiZHVd_2KJtIXOW0zFhFfBaJJilH51) | S2021 |\n| 75. | **Deep Learning** | Alfredo Canziani and Yann LeCun, NYU | [NYU-DLSP21](https://atcold.github.io/NYU-DLSP21) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLLHTzKZzVU9e6xUfG10TkTWApKSZCzuBI) | S2021 |\n| 76. | **Applied Deep Learning** | Alexander Pacha, TU Wien | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLNsFwZQ_pkE8xNYTEyorbaWPN7nvbWyk1) | 2020-2021 |\n| 77. | **Machine Learning** | Hung-yi Lee, National Taiwan University | [ML'21](https://speech.ee.ntu.edu.tw/~hylee/ml/2021-spring.php) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLJV_el3uVTsNxV_IGauQZBHjBKZ26JHjd) | S2021 |\n| 78. | **Mathematics of Deep Learning** | Lots of legends, FAU | [MoDL](https://www.fau.tv/course/id/878) | [Lecture-Videos](https://www.fau.tv/course/id/878) | 2019-21 |\n| 79. | **Deep Learning** | Peter Bloem, Michael Cochez, and Jakub Tomczak, VU-Amsterdam | [DL](https://dlvu.github.io/) | [YouTube-Lectures](https://www.youtube.com/channel/UCYh1zKnwzrSjrO2Ae-akfTg/playlists) | 2020-21 |\n| 80. | **Applied Deep Learning** | Maziar Raissi, UC Boulder | [ADL'21](https://github.com/maziarraissi/Applied-Deep-Learning) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLoEMreTa9CNmuxQeIKWaz7AVFd_ZeAcy4) | 2021 |\n| | | | | | |\n| 81. | **An Introduction to Group Equivariant Deep Learning** | Erik J. Bekkers, Universiteit van Amsterdam | [UvAGEDL](https://uvagedl.github.io) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL8FnQMH2k7jzPrxqdYufoiYVHim8PyZWd) | 2022 |\n| | | | | | |\n\n[Go to Contents :arrow_heading_up:](https://github.com/kmario23/deep-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/Instructor(s) | Course Webpage | Video Lectures | Year |\n| ---- | ------------------------------------------------------------ | ------------------------------------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ---------- |\n| 1. | **Linear Algebra** | Gilbert Strang, MIT | [18.06 SC](http://ocw.mit.edu/18-06SCF11) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL221E2BBF13BECF6C) | 2011 |\n| 2. | **Probability Primer** | Jeffrey Miller, Brown University | `mathematical monk` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL17567A1A3F5DB5E4) | 2011 |\n| 3. | **Information Theory, Pattern Recognition, and Neural Networks** | David Mackay, University of Cambridge | [ITPRNN](http://www.inference.org.uk/mackay/itprnn) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLruBu5BI5n4aFpG32iMbdWoRVAA-Vcso6) | 2012 |\n| 4. | **Linear Algebra Review** | Zico Kolter, CMU | [LinAlg](http://www.cs.cmu.edu/~zkolter/course/linalg/index.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLM4Pv4KYYzGzL5ay6dmpyzRnbzQ__8v_t) | 2013 |\n| 5. | **Probability and Statistics** | Michel van Biezen | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLX2gX-ftPVXUWwTzAkOhBdhplvz0fByqV) | 2015 |\n| 6. | **Linear Algebra: An in-depth Introduction** | Pavel Grinfeld | `None` | [Part-1](https://www.youtube.com/playlist?list=PLlXfTHzgMRUKXD88IdzS14F4NxAZudSmv) \u003cbr/\u003e [Part-2](https://www.youtube.com/playlist?list=PLlXfTHzgMRULWJYthculb2QWEiZOkwTSU) \u003cbr/\u003e [Part-3](https://www.youtube.com/playlist?list=PLlXfTHzgMRUIqYrutsFXCOmiqKUgOgGJ5) \u003cbr/\u003e [Part-4](https://www.youtube.com/playlist?list=PLlXfTHzgMRULZfrNCrrJ7xDcTjGr633mm) | 2015- 2017 |\n| 7. | **Multivariable Calculus** | Grant Sanderson, Khan Academy | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLSQl0a2vh4HC5feHa6Rc5c0wbRTx56nF7) | 2016 |\n| 8. | **Essence of Linear Algebra** | Grant Sanderson | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab) | 2016 |\n| 9. | **Essence of Calculus** | Grant Sanderson | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLZHQObOWTQDMsr9K-rj53DwVRMYO3t5Yr) | 2017-2018 |\n| 10. | **Math Background for Machine Learning** | Geoff Gordon, CMU | [10-606](https://canvas.cmu.edu/courses/603/assignments/syllabus), [10-607](https://piazza.com/cmu/fall2017/1060610607/home) | [YouTube-Lectures](https://www.youtube.com/playlist?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://www.coursera.org/learn/linear-algebra-machine-learning) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLmAuaUS7wSOP-iTNDivR0ANKuTUhEzMe4) | 2018 |\n| 12. | **Multivariable Calculus** | S.K. Gupta and Sanjeev Kumar, IIT-Roorkee | [MVC](https://nptel.ac.in/syllabus/111107108/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLq-Gm0yRYwTiQtK374NzhFOcQkWmJ71vx) | 2018 |\n| 13. | **Engineering Probability** | Rich Radke, Rensselaer Polytechnic Institute | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLuh62Q4Sv7BU1dN2G6ncyiMbML7OXh_Jx) | 2018 |\n| 14. | **Matrix Methods in Data Analysis, Signal Processing, and Machine Learning** | Gilbert Strang, MIT | [18.065](https://ocw.mit.edu/courses/mathematics/18-065-matrix-methods-in-data-analysis-signal-processing-and-machine-learning-spring-2018) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLUl4u3cNGP63oMNUHXqIUcrkS2PivhN3k) | S2018 |\n| 15. | **Information Theory** | Himanshu Tyagi, IISC, Bengaluru | [E2 201](https://ece.iisc.ac.in/~htyagi/course-E2201-2020.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLgMDNELGJ1CYS-8dlMGPIaowVfeda4nUj) | 2018-20 |\n| 16. | **Math Camp** | Mark Walker, University of Arizona | [UAMathCamp / Econ-519](http://www.u.arizona.edu/~mwalker/MathCamp2019.htm) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLcjqUUQt__ZGLhwUacPm7_RKs2eJNFwco) | 2019 |\n| 17. | **A 2020 Vision of Linear Algebra** | Gilbert Strang, MIT | [VoLA](https://ocw.mit.edu/resources/res-18-010-a-2020-vision-of-linear-algebra-spring-2020/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLUl4u3cNGP61iQEFiWLE21EJCxwmWvvek) | S2020 |\n| 18. | **Mathematics for Numerical Computing and Machine Learning** | Szymon Rusinkiewicz, Princeton University | [COS-302](https://www.cs.princeton.edu/courses/archive/fall20/cos302/outline.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL88aSuXxl_dSjC5pIG8bGkC5wsUPyW_Hh) | F2020 |\n| 19. | **Essential Statistics for Neuroscientists** | Philipp Berens, Universität Klinikum Tübingen | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL05umP7R6ij0Gw5SLIrOA1dMYScCx4oXT) | 2020 |\n| 20. | **Mathematics for Machine Learning** | Ulrike von Luxburg, Eberhard Karls Universität Tübingen | [Math4ML](https://www.tml.cs.uni-tuebingen.de/teaching/2020_maths_for_ml) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL05umP7R6ij1a6KdEy8PVE9zoCv6SlHRS) | W2020 |\n| 21. | **Introduction to Causal Inference** | Brady Neal, Mila, Montréal | [CausalInf](https://www.bradyneal.com/causal-inference-course) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLoazKTcS0Rzb6bb9L508cyJ1z-U9iWkA0) | F2020 |\n| 22. | **Applied Linear Algebra** | Andrew Thangaraj, IIT Madras | [EE5120](http://www.ee.iitm.ac.in/~andrew/EE5120) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLyqSpQzTE6M-CHZU5RGfamcXOnuFyTOpm) | 2021 |\n| 23. | **Mathematical Tools for Data Science** | Carlos Fernandez-Granda, New York University | [DS-GA 1013/Math-GA 2824](https://cds.nyu.edu/math-tools) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLBEf5mJtE6KtU6YlXFZD6lyYcHhW5pIlc) | 2021 |\n| 24. | **Mathematics for Numerical Computing and Machine Learning** | Ryan Adams, Princeton University | [COS 302 / SML 305](https://www.cs.princeton.edu/courses/archive/spring21/cos302) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLCO4cUaBLHFEHo42HVIVWaSOvbAiH30uc) | 2021 |\n| | | | | | |\n| | | | | | |\n\n[Go to Contents :arrow_heading_up:](https://github.com/kmario23/deep-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/Instructor(s) | Course Webpage | Video Lectures | Year |\n| ---- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | ---------- |\n| 1. | **Convex Optimization** | Stephen Boyd, Stanford University | [ee364a](http://web.stanford.edu/class/ee364a/lectures.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL3940DD956CDF0622) | 2008 |\n| 2. | **Introduction to Optimization** | Michael Zibulevsky, Technion | [CS-236330](https://sites.google.com/site/michaelzibulevsky/optimization-course) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLDFB2EEF4DDAFE30B) | 2009 |\n| 3. | **Optimization for Machine Learning** | S V N Vishwanathan, Purdue University | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL09B0E8AFC69BE108) | 2011 |\n| 4. | **Optimization** | Geoff Gordon \u0026 Ryan Tibshirani, CMU | [10-725](https://www.cs.cmu.edu/~ggordon/10725-F12/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL7y-1rk2cCsDOv91McLOnV4kExFfTB7dU) | 2012 |\n| 5. | **Convex Optimization** | Joydeep Dutta, IIT-Kanpur | [cvx-nptel](https://nptel.ac.in/courses/111/104/111104068) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLbMVogVj5nJQHFqfiSdgaLCCWvDcm1W4l) | 2013 |\n| 6. | **Foundations of Optimization** | Joydeep Dutta, IIT-Kanpur | [fop-nptel](https://nptel.ac.in/courses/111/104/111104071) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLbMVogVj5nJRRbofh3Qm3P6_NVyevDGD_) | 2014 |\n| 7. | **Algorithmic Aspects of Machine Learning** | Ankur Moitra, MIT | [18.409-AAML](http://people.csail.mit.edu/moitra/409.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLB3sDpSRdrOvI1hYXNsa6Lety7K8FhPpx) | S2015 |\n| 8. | **Numerical Optimization** | Shirish K. Shevade, IISC | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL6EA0722B99332589) | 2015 |\n| 9. | **Convex Optimization** | Ryan Tibshirani, CMU | [10-725](https://www.stat.cmu.edu/~ryantibs/convexopt-S15/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLjbUi5mgii6BZBhJ9nW7eydgycyCOYeZ6) | S2015 |\n| 10. | **Convex Optimization** | Ryan Tibshirani, CMU | [10-725](http://stat.cmu.edu/~ryantibs/convexopt-F15/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLjbUi5mgii6AGJW3La3BpEXe27n8v3biT) | F2015 |\n| 11. | **Advanced Algorithms** | Ankur Moitra, MIT | [6.854-AA](http://people.csail.mit.edu/moitra/854.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL6ogFv-ieghdoGKGg2Bik3Gl1glBTEu8c) | S2016 |\n| 12. | **Introduction to Optimization** | Michael Zibulevsky, Technion | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLBD31626529B0AC2A) | 2016 |\n| 13. | **Convex Optimization** | Javier Peña \u0026 Ryan Tibshirani | [10-725/36-725](https://www.stat.cmu.edu/~ryantibs/convexopt-F16) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLjbUi5mgii6AVdvImLB9-Hako68p9MpIC) | F2016 |\n| 14. | **Convex Optimization** | Ryan Tibshirani, CMU | [10-725](https://www.stat.cmu.edu/~ryantibs/convexopt-F18/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLpIxOj-HnDsMM7BCNGC3hPFU3DfCWfVIw) \u003cbr/\u003e [Lecture-Videos](https://www.stat.cmu.edu/~ryantibs/convexopt-F18/) | F2018 |\n| 15. | **Modern Algorithmic Optimization** | Yurii Nesterov, UCLouvain | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLEqoHzpnmTfAoUDqnmMly-KgyJ6ZM_axf) | 2018 |\n| 16. | **Optimization, Foundations of Optimization** | Mark Walker, University of Arizona | [MathCamp-20](http://www.u.arizona.edu/~mwalker/MathCamp2020/MathCamp2020LectureNotes.htm) | [YouTube-Lectures-Found.](https://www.youtube.com/playlist?list=PLcjqUUQt__ZE6wp_c4-FcRdmzBvx8VN7O) \u003cbr/\u003e [YouTube-Lectures-Opt](https://www.youtube.com/playlist?list=PLcjqUUQt__ZE0ZSTNRyBIgLJ5obPHdmxC) | 2019 - now |\n| 17. | **Optimization: Principles and Algorithms** | Michel Bierlaire, École polytechnique fédérale de Lausanne (EPFL) | [opt-algo](https://transp-or.epfl.ch/books/optimization/html/about_book.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLM4Pv4KYYzGzOpWwsaV6GgllT6njsi1G-) | 2019 |\n| 18. | **Optimization and Simulation** | Michel Bierlaire, École polytechnique fédérale de Lausanne (EPFL) | [opt-sim](https://transp-or.epfl.ch/courses/OptSim2019/slides.php) | [YouTube-Lectures](https://www.youtube.com/playlist?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://impa.br/eventos-do-impa/eventos-2019/xiii-brazilian-workshop-on-continuous-optimization) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLo4jXE-LdDTQVZhnLPq2W31vJ1fq1VSp6) | 2019 |\n| 20. | **One World Optimization Seminar** | Lots of Legends, Universität Wien | [1W-OPT](https://owos.univie.ac.at) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLBQo-yZOMzLWEcAptzTYOnwXo9hhXrAa2) | 2020- |\n| | | | | | |\n| 21. | **Convex Optimization II** | Constantine Caramanis, UT Austin | [CVX-Optim-II](http://users.ece.utexas.edu/~cmcaram/constantine_caramanis/Announcements.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLXsmhnDvpjORzPelSDs0LSDrfJcqyLlZc) | S2020 |\n| 22. | **Combinatorial Optimization** | Constantine Caramanis, UT Austin | [comb-op](https://caramanis.github.io/teaching/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLXsmhnDvpjORcTRFMVF3aUgyYlHsxfhNL) | F2020 |\n| 23. | **Optimization Methods for Machine Learning and Engineering** | Julius Pfrommer, Jürgen Beyerer, Karlsruher Institut für Technologie (KIT) | [Optim-MLE](https://ies.iar.kit.edu/lehre_1487.php), [slides](https://drive.google.com/drive/folders/1WWVWV4vDBIOkjZc6uFY3nfXvpaOUHcfb) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLdkTDauaUnQpzuOCZyUUZc0lxf4-PXNR5) | W2020-21 |\n| | | | | | |\n\n[Go to Contents :arrow_heading_up:](https://github.com/kmario23/deep-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/Instructor(s) | Course Webpage | Video Lectures | Year |\n| ---- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | --------- |\n| 1. | **CS229: Machine Learning** | Andrew Ng, Stanford University | [CS229-old](https://see.stanford.edu/Course/CS229/) \u003cbr/\u003e [CS229-new](http://cs229.stanford.edu/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLA89DCFA6ADACE599) | 2007 |\n| 2. | **Machine Learning** | Jeffrey Miller, Brown University | `mathematical monk` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLD0F06AA0D2E8FFBA) | 2011 |\n| 3. | **Machine Learning** | Tom Mitchell, CMU | [10-701](http://www.cs.cmu.edu/~tom/10701_sp11/) | [Lecture-Videos](http://www.cs.cmu.edu/~tom/10701_sp11/lectures.shtml) | 2011 |\n| 4. | **Machine Learning and Data Mining** | Nando de Freitas, University of British Columbia | [CPSC-340](https://www.cs.ubc.ca/~nando/340-2012/index.php) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLE6Wd9FR--Ecf_5nCbnSQMHqORpiChfJf) | 2012 |\n| 5. | **Learning from Data** | Yaser Abu-Mostafa, CalTech | [CS156](http://work.caltech.edu/telecourse.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLD63A284B7615313A) | 2012 |\n| 6. | **Machine Learning** | Rudolph Triebel, Technische Universität München | [Machine Learning](https://vision.in.tum.de/teaching/ws2013/ml_ws13) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLTBdjV_4f-EIiongKlS9OKrBEp8QR47Wl) | 2013 |\n| 7. | **Introduction to Machine Learning** | Alex Smola, CMU | [10-701](http://alex.smola.org/teaching/cmu2013-10-701/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLZSO_6-bSqHQmMKwWVvYwKreGu4b4kMU9) | 2013 |\n| 8. | **Introduction to Machine Learning** | Alex Smola and Geoffrey Gordon, CMU | [10-701x](http://alex.smola.org/teaching/cmu2013-10-701x/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLZSO_6-bSqHR7NPk4k0zqdm2dPdraQZ_B) | 2013 |\n| 9. | **Pattern Recognition** | Sukhendu Das, IIT-M and C.A. Murthy, ISI-Calcutta | [PR-NPTEL](https://nptel.ac.in/syllabus/106106046/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLbMVogVj5nJQJMLb2CYw9rry0d5s0TQRp) | 2014 |\n| 10. | **An Introduction to Statistical Learning with Applications in R** | Trevor Hastie and Robert Tibshirani, Stanford | [stat-learn](https://lagunita.stanford.edu/courses/HumanitiesandScience/StatLearning/Winter2015/about) \u003cbr/\u003e [R-bloggers](https://www.r-bloggers.com/in-depth-introduction-to-machine-learning-in-15-hours-of-expert-videos/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLOg0ngHtcqbPTlZzRHA2ocQZqB1D_qZ5V) | 2014 |\n| | | | | | |\n| 11. | **Introduction to Machine Learning** | Katie Malone, Sebastian Thrun, Udacity | [ML-Udacity](https://www.udacity.com/course/ud120) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLAwxTw4SYaPkQXg8TkVdIvYv4HfLG7SiH) | 2015 |\n| 12. | **Introduction to Machine Learning** | Dhruv Batra, Virginia Tech | [ECE-5984](https://filebox.ece.vt.edu/~s15ece5984/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL-fZD610i7yDUiNTFy-tEOxkTwg4mHZHu) | 2015 |\n| 13. | **Statistical Learning - Classification** | Ali Ghodsi, University of Waterloo | [STAT-441](https://uwaterloo.ca/data-analytics/statistical-learning-classification) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLehuLRPyt1Hy-4ObWBK4Ab0xk97s6imfC) | 2015 |\n| 14. | **Machine Learning Theory** | Shai Ben-David, University of Waterloo | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLPW2keNyw-usgvmR7FTQ3ZRjfLs5jT4BO) | 2015 |\n| 15. | **Introduction to Machine Learning** | Alex Smola, CMU | [10-701](http://alex.smola.org/teaching/10-701-15/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLZSO_6-bSqHTTV7w9u7grTXBHMH-mw3qn) | S2015 |\n| 16. | **Statistical Machine Learning** | Larry Wasserman, CMU | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLjbUi5mgii6BWEUZf7He6nowWvGne_Y8r) | S2015 |\n| 17. | **ML: Supervised Learning** | Michael Littman, Charles Isbell, Pushkar Kolhe, GaTech | [ML-Udacity](https://eu.udacity.com/course/machine-learning--ud262) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLAwxTw4SYaPl0N6-e1GvyLp5-MUMUjOKo) | 2015 |\n| 18. | **ML: Unsupervised Learning** | Michael Littman, Charles Isbell, Pushkar Kolhe, GaTech | [ML-Udacity](https://eu.udacity.com/course/machine-learning-unsupervised-learning--ud741) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLAwxTw4SYaPmaHhu-Lz3mhLSj-YH-JnG7) | 2015 |\n| 19. | **Advanced Introduction to Machine Learning** | Barnabas Poczos and Alex Smola | [10-715](https://www.cs.cmu.edu/~bapoczos/Classes/ML10715_2015Fall/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL4YhK0pT0ZhWBzSBkMGzpnPw6sf6Ma0IX) | F2015 |\n| 20. | **Machine Learning** | Pedro Domingos, UWashington | [CSEP-546](https://courses.cs.washington.edu/courses/csep546/16sp/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLTPQEx-31JXgtDaC6-3HxWcp7fq4N8YGr) | S2016 |\n| | | | | | |\n| 21. | **Statistical Machine Learning** | Larry Wasserman, CMU | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLTB9VQq8WiaCBK2XrtYn5t9uuPdsNm7YE) | S2016 |\n| 22. | **Machine Learning with Large Datasets** | William Cohen, CMU | [10-605](http://curtis.ml.cmu.edu/w/courses/index.php/Machine_Learning_with_Large_Datasets_10-605_in_Fall_2016) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLnfBqXRW5MRhPtfkadfwQ0VcuSi2IwEcW) | F2016 |\n| 23. | **Math Background for Machine Learning** | Geoffrey Gordon, CMU | `10-600` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL7y-1rk2cCsA339crwXMWUaBRuLBvPBCg) | F2016 |\n| 24. | **Statistical Learning - Classification** | Ali Ghodsi, University of Waterloo | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLehuLRPyt1HzXDemu7K4ETcF0Ld_B5adG) | 2017 |\n| 25. | **Machine Learning** | Andrew Ng, Stanford University | [Coursera-ML](https://www.coursera.org/learn/machine-learning) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLLssT5z_DsK-h9vYZkQkYNWcItqhlRJLN) | 2017 |\n| 26. | **Machine Learning** | Roni Rosenfield, CMU | [10-601](http://www.cs.cmu.edu/~roni/10601-f17/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL7k0r4t5c10-g7CWCnHfZOAxLaiNinChk) | 2017 |\n| 27. | **Statistical Machine Learning** | Ryan Tibshirani, Larry Wasserman, CMU | [10-702](http://www.stat.cmu.edu/~ryantibs/statml/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLjbUi5mgii6B7A0nM74zHTOVQtTC9DaCv) | S2017 |\n| 28. | **Machine Learning for Computer Vision** | Fred Hamprecht, Heidelberg University | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLuRaSnb3n4kSQFyt8VBldsQ9pO9Xtu8rY) | F2017 |\n| 29. | **Math Background for Machine Learning** | Geoffrey Gordon, CMU | [10-606 / 10-607](https://canvas.cmu.edu/courses/603/assignments/syllabus) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL7y-1rk2cCsAqRtWoZ95z-GMcecVG5mzA) | F2017 |\n| 30. | **Data Visualization** | Ali Ghodsi, University of Waterloo | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLehuLRPyt1HzQoXEhtNuYTmd0aNQvtyAK) | 2017 |\n| | | | | | |\n| 31. | **Machine Learning for Physicists** | Florian Marquardt, Uni Erlangen-Nürnberg | [ML4Phy-17](http://www.thp2.nat.uni-erlangen.de/index.php/2017_Machine_Learning_for_Physicists,_by_Florian_Marquardt) | [Lecture-Videos](https://www.video.uni-erlangen.de/course/id/574) | 2017 |\n| 32. | **Machine Learning for Intelligent Systems** | Kilian Weinberger, Cornell University | [CS4780](http://www.cs.cornell.edu/courses/cs4780/2018fa/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLl8OlHZGYOQ7bkVbuRthEsaLr7bONzbXS) | F2018 |\n| 33. | **Statistical Learning Theory and Applications** | Tomaso Poggio, Lorenzo Rosasco, Sasha Rakhlin | [9.520/6.860](https://cbmm.mit.edu/lh-9-520) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLyGKBDfnk-iAtLO6oLW4swMiQGz4f2OPY) | F2018 |\n| 34. | **Machine Learning and Data Mining** | Mike Gelbart, University of British Columbia | [CPSC-340](https://ubc-cs.github.io/cpsc340/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLWmXHcz_53Q02ZLeAxigki1JZFfCO6M-b) | 2018 |\n| 35. | **Foundations of Machine Learning** | David Rosenberg, Bloomberg | [FOML](https://bloomberg.github.io/foml/#home) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLnZuxOufsXnvftwTB1HL6mel1V32w0ThI) | 2018 |\n| 36. | **Introduction to Machine Learning** | Andreas Krause, ETH Zürich | [IntroML](https://las.inf.ethz.ch/teaching/introml-s18) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLzn6LN6WhlN273tsqyfdrBUsA-o5nUESV) | 2018 |\n| 37. | **Machine Learning Fundamentals** | Sanjoy Dasgupta, UC-San Diego | [MLF-slides](https://drive.google.com/drive/folders/1l1rwv-jMihLZIpW0zTgGN9-snWOsA3M9) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL_onPhFCkVQhUzcTVgQiC8W2ShZKWlm0s) | 2018 |\n| 38. | **Machine Learning** | Jordan Boyd-Graber, University of Maryland | [CMSC-726](http://users.umiacs.umd.edu/~jbg/teaching/CMSC_726/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLegWUnz91WfsELyRcZ7d1GwAVifDaZmgo) | 2015-2018 |\n| 39. | **Machine Learning** | Andrew Ng, Stanford University | [CS229](http://cs229.stanford.edu/syllabus-autumn2018.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLoROMvodv4rMiGQp3WXShtMGgzqpfVfbU) | 2018 |\n| 40. | **Machine Intelligence** | H.R.Tizhoosh, UWaterloo | [SYDE-522](https://kimialab.uwaterloo.ca/kimia/index.php/teaching/syde-522-machine-intelligence-2) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL4upCU5bnihwCX93Gv6AQnKmVMwx4AZoT) | 2019 |\n| | | | | | |\n| 41. | **Introduction to Machine Learning** | Pascal Poupart, University of Waterloo | [CS480/680](https://cs.uwaterloo.ca/~ppoupart/teaching/cs480-spring19) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLdAoL1zKcqTW-uzoSVBNEecKHsnug_M0k) | S2019 |\n| 42. | **Advanced Machine Learning** | Thorsten Joachims, Cornell University | [CS-6780](https://www.cs.cornell.edu/courses/cs6780/2019sp) | [Lecture-Videos](https://cornell.mediasite.com/Mediasite/Catalog/Full/f5d1cd3323f746cca80b2468bf97efd421) | S2019 |\n| 43. | **Machine Learning for Structured Data** | Matt Gormley, Carnegie Mellon University | [10-418/10-618](http://www.cs.cmu.edu/~mgormley/courses/10418/schedule.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL4CxkUJbvNVihRKP4bXufvRLIWzeS-ieP) | F2019 |\n| 44. | **Advanced Machine Learning** | Joachim Buhmann, ETH Zürich | [ML2-AML](https://ml2.inf.ethz.ch/courses/aml/) | [Lecture-Videos](https://video.ethz.ch/lectures/d-infk/2019/autumn/252-0535-00L.html) | F2019 |\n| 45. | **Machine Learning for Signal Processing** | Vipul Arora, IIT-Kanpur | [MLSP](http://home.iitk.ac.in/~vipular/stuff/2019_MLSP.html) | [Lecture-Videos](https://iitk-my.sharepoint.com/:f:/g/personal/vipular_iitk_ac_in/Enf97NZfsoVBiyclC6yHfe4BlUv6CA4U8LPQQ4vtsDo_Xg) | F2019 |\n| 46. | **Foundations of Machine Learning** | Animashree Anandkumar, CalTech | [CMS-165](http://tensorlab.cms.caltech.edu/users/anima/cms165-2019.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLVNifWxslHCA5GUh0o92neMiWiQiGVFqp) | 2019 |\n| 47. | **Machine Learning for Physicists** | Florian Marquardt, Uni Erlangen-Nürnberg | `None` | [Lecture-Videos](https://www.video.uni-erlangen.de/course/id/778) | 2019 |\n| 48. | **Applied Machine Learning** | Andreas Müller, Columbia University | [COMS-W4995](https://www.cs.columbia.edu/~amueller/comsw4995s19/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL_pVmAaAnxIQGzQS2oI3OWEPT-dpmwTfA) | 2019 |\n| 49. | **Fundamentals of Machine Learning over Networks** | Hossein Shokri-Ghadikolaei, KTH, Sweden | [MLoNs](https://sites.google.com/view/mlons/course-materials) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLWoZTd81WFCEBFrxDfNUrDnt3ABdLfg80) | 2019 |\n| 50. | **Foundations of Machine Learning and Statistical Inference** | Animashree Anandkumar, CalTech | [CMS-165](http://tensorlab.cms.caltech.edu/users/anima/cms165-2020.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLVNifWxslHCDlbyitaLLYBOAEPbmF1AHg) | 2020 |\n| | | | | | |\n| 51. | **Machine Learning** | Rebecca Willett and Yuxin Chen, University of Chicago | [STAT 37710 / CMSC 35400](https://voices.uchicago.edu/willett/teaching/stats37710-cmsc35400-s20) | [Lecture-Videos](https://voices.uchicago.edu/willett/teaching/stats37710-cmsc35400-s20) | S2020 |\n| 52. | **Introduction to Machine Learning** | Sanjay Lall and Stephen Boyd, Stanford University | [EE104/CME107](http://ee104.stanford.edu) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLoROMvodv4rN_Uy7_wmS051_q1d6akXmK) | S2020 |\n| 53. | **Applied Machine Learning** | Andreas Müller, Columbia University | [COMS-W4995](https://www.cs.columbia.edu/~amueller/comsw4995s20/schedule/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL_pVmAaAnxIRnSw6wiCpSvshFyCREZmlM) | S2020 |\n| 54. | **Statistical Machine Learning** | Ulrike von Luxburg, Eberhard Karls Universität Tübingen | [Stat-ML](https://www.tml.cs.uni-tuebingen.de/teaching/2020_statistical_learning/index.php) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL05umP7R6ij2XCvrRzLokX6EoHWaGA2cC) | SS2020 |\n| 55. | **Probabilistic Machine Learning** | Philipp Hennig, Eberhard Karls Universität Tübingen | [Prob-ML](https://uni-tuebingen.de/en/180804) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL05umP7R6ij1tHaOFY96m5uX3J21a6yNd) | SS2020 |\n| 56. | **Machine Learning** | Sarath Chandar, PolyMTL, UdeM, Mila | [INF8953CE](http://sarathchandar.in/teaching/ml/fall2020) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLImtCgowF_ET0mi-AmmqQ0SIJUpWYaIOr) | F2020 |\n| 57. | **Machine Learning** | Erik Bekkers, Universiteit van Amsterdam | [UvA-ML](https://uvaml1.github.io/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL8FnQMH2k7jzhtVYbKmvrMyXDYMmgjj_n) | F2020 |\n| 58. | **Neural Networks for Signal Processing** | Shayan Srinivasa Garani, Indian Institute of Science | [NN4SP](https://labs.dese.iisc.ac.in/pnsil/neural-networks-and-learning-systems-i-fall-2020/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLgMDNELGJ1CZn1399dV7_U4VBNJflRsua) | F2020 |\n| 59. | **Introduction to Machine Learning** | Dmitry Kobak, Universität Klinikum Tübingen | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL05umP7R6ij35ShKLDqccJSDntugY4FQT) | 2020 |\n| 60. | **Machine Learning (PRML)** | Erik J. Bekkers, Universiteit van Amsterdam | [UvAML-1](https://uvaml1.github.io) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL8FnQMH2k7jzhtVYbKmvrMyXDYMmgjj_n) | 2020 |\n| | | | | | |\n| 61. | **Machine Learning with Kernel Methods** | Julien Mairal and Jean-Philippe Vert, Inria/ENS Paris-Saclay, Google | [ML-Kernels](http://members.cbio.mines-paristech.fr/~jvert/svn/kernelcourse/course/2021mva/index.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLD93kGj6_EdrkNj27AZMecbRlQ1SMkp_o) | S2021 |\n| 62. | **Continual Learning** | Vincenzo Lomonaco, Università di Pisa | [ContLearn'21](https://course.continualai.org/background/details) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLm6QXeaB-XkBfM5RgQP6wCR7Jegdg51Px) | 2021 |\n| 63. | **Causality** | Christina Heinze-Deml, ETH Zurich | [Causal'21](https://stat.ethz.ch/lectures/ss21/causality.php#course_materials) | [YouTube-Lectures](https://stat.ethz.ch/lectures/ss21/causality.php#course_materials) | 2021 |\n| | | | | | |\n\n[Go to Contents :arrow_heading_up:](https://github.com/kmario23/deep-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/Instructor(s) | Course Webpage | Video Lectures | Year |\n| ---- | -------------------------------------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | ------ |\n| 1. | **A Short Course on Reinforcement Learning** | Satinder Singh, UMichigan | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLM4Pv4KYYzGy4cIFQ5C36-1jMNLab80Ky) | 2011 |\n| 2. | **Approximate Dynamic Programming** | Dimitri P. Bertsekas, MIT | [Lecture-Slides](http://adpthu2014.weebly.com/slides--materials.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLiCLbsFQNFAxOmVeqPhI5er1LGf2-L9I4) | 2014 |\n| 3. | **Introduction to Reinforcement Learning** | David Silver, DeepMind | [UCL-RL](http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLqYmG7hTraZDM-OYHWgPebj2MfCFzFObQ) | 2015 |\n| 4. | **Reinforcement Learning** | Charles Isbell, Chris Pryby, GaTech; Michael Littman, Brown | [RL-Udacity](https://eu.udacity.com/course/reinforcement-learning--ud600) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLAwxTw4SYaPnidDwo9e2c7ixIsu_pdSNp) | 2015 |\n| 5. | **Reinforcement Learning** | Balaraman Ravindran, IIT Madras | [RL-IITM](https://www.cse.iitm.ac.in/~ravi/courses/Reinforcement%20Learning.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLNdWVHi37UggQIVcaZcmtGGEQHY9W7d9D) | 2016 |\n| 6. | **Deep Reinforcement Learning** | Sergey Levine, UC Berkeley | [CS-294](http://rail.eecs.berkeley.edu/deeprlcoursesp17/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLkFD6_40KJIwTmSbCv9OVJB3YaO4sFwkX) | S2017 |\n| 7. | **Deep Reinforcement Learning** | Sergey Levine, UC Berkeley | [CS-294](http://rail.eecs.berkeley.edu/deeprlcourse-fa17/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLkFD6_40KJIznC9CDbVTjAF2oyt8_VAe3) | F2017 |\n| 8. | **Deep RL Bootcamp** | Many legends, UC Berkeley | [Deep-RL](https://sites.google.com/view/deep-rl-bootcamp/lectures) | [YouTube-Lectures](https://www.youtube.com/channel/UCTgM-VlXKuylPrZ_YGAJHOw/videos) | 2017 |\n| 9 | **Data Efficient Reinforcement Learning** | Lots of Legends, Canary Islands | [DERL-17](http://dalimeeting.org/dali2017/data-efficient-reinforcement-learning.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL-tWvTpyd1VAvDpxukup6w-SuZQQ7e8K8) | 2017 |\n| 10. | **Deep Reinforcement Learning** | Sergey Levine, UC Berkeley | [CS-294-112](http://rail.eecs.berkeley.edu/deeprlcourse-fa18/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLkFD6_40KJIxJMR-j5A1mkxK26gh_qg37) | 2018 |\n| | | | | | |\n| 11. | **Reinforcement Learning** | Pascal Poupart, University of Waterloo | [CS-885](https://cs.uwaterloo.ca/~ppoupart/teaching/cs885-spring18/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLdAoL1zKcqTXFJniO3Tqqn6xMBBL07EDc) | 2018 |\n| 12. | **Deep Reinforcement Learning and Control** | Katerina Fragkiadaki and Tom Mitchell, CMU | [10-703](http://www.andrew.cmu.edu/course/10-703/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLpIxOj-HnDsNfvOwRKLsUobmnF2J1l5oV) | 2018 |\n| 13. | **Reinforcement Learning and Optimal Control** | Dimitri Bertsekas, Arizona State University | [RLOC](http://web.mit.edu/dimitrib/www/RLbook.html) | [Lecture-Videos](http://web.mit.edu/dimitrib/www/RLbook.html) | 2019 |\n| 14. | **Reinforcement Learning** | Emma Brunskill, Stanford University | [CS 234](http://web.stanford.edu/class/cs234/index.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLoROMvodv4rOSOPzutgyCTapiGlY2Nd8u) | 2019 |\n| 15. | **Reinforcement Learning Day** | Lots of Legends, Microsoft Research, New York | [RLD-19](https://www.microsoft.com/en-us/research/event/reinforcement-learning-day-2019/#!agenda) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLD7HFcN7LXRe9nWEX3Up-RiCDi6-0mqVC) | 2019 |\n| 16. | **New Directions in Reinforcement Learning and Control** | Lots of Legends, IAS, Princeton University | [NDRLC-19](https://www.math.ias.edu/ndrlc) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLdDZb3TwJPZ61sGqd6cbWCmTc275NrKu3) | 2019 |\n| 17. | **Deep Reinforcement Learning** | Sergey Levine, UC Berkeley | [CS 285](http://rail.eecs.berkeley.edu/deeprlcourse-fa19) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLkFD6_40KJIwhWJpGazJ9VSj9CFMkb79A) | F2019 |\n| 18. | **Deep Multi-Task and Meta Learning** | Chelsea Finn, Stanford University | [CS 330](https://cs330.stanford.edu/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLoROMvodv4rMC6zfYmnD7UG3LVvwaITY5) | F2019 |\n| 19. | **RL-Theory Seminars** | Lots of Legends, Earth | [RL-theory-sem](https://sites.google.com/view/rltheoryseminars/past-seminars) | [YouTube-Lectures](https://www.youtube.com/channel/UCfBFutC9RbKK6p--B4R9ebA/videos) | 2020 - |\n| 20. | **Deep Reinforcement Learning** | Sergey Levine, UC Berkeley | [CS 285](http://rail.eecs.berkeley.edu/deeprlcourse-fa20) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL_iWQOsE6TfURIIhCrlt-wj9ByIVpbfGc) | F2020 |\n| | | | | | |\n| 21. | **Introduction to Reinforcement Learning** | Amir-massoud Farahmand, Vector Institute, University of Toronto | [RL-intro](https://amfarahmand.github.io/IntroRL) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLCveiXxL2xNbiDq51a8iJwPRq2aO0ykrq) | S2021 |\n| 22. | **Reinforcement Learning** | Antonio Celani and Emanuele Panizon, International Centre for Theoretical Physics | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLp0hSY2uBeP8q2G3mfHGVGvQFEMX0QRWM) | 2021 |\n| 23. | **Computational Sensorimotor Learning** | Pulkit Agrawal, MIT-CSAIL | [6.884-CSL](https://pulkitag.github.io/6.884/lectures) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLwNwxAG-kBxPMTIs2fKWSsf7HqL2TcC78) | S2021 |\n| 24. | **Reinforcement Learning** | Dimitri P. Bertsekas, ASU/MIT | [RL-21](http://web.mit.edu/dimitrib/www/RLbook.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLmH30BG15SIp79JRJ-MVF12uvB1qPtPzn) | S2021 |\n| 25. | **Reinforcement Learning** | Sarath Chandar, École Polytechnique de Montréal | [INF8953DE](https://chandar-lab.github.io/INF8953DE) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLImtCgowF_ES_JdF_UcM60EXTcGZg67Ua) | F2021 |\n| 26. | **Deep Reinforcement Learning** | Sergey Levine, UC Berkeley | [CS 285](http://rail.eecs.berkeley.edu/deeprlcourse) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL_iWQOsE6TfXxKgI1GgyV1B_Xa0DxE5eH) | F2021 |\n| 27. | **Reinforcement Learning Lecture Series** | Lots of Legends, DeepMind \u0026 UC London | [RL-series](https://deepmind.com/learning-resources/reinforcement-learning-series-2021) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLqYmG7hTraZDVH599EItlEWsUOsJbAodm) | 2021 |\n| 28. | **Reinforcement Learning** | Dimitri P. Bertsekas, ASU/MIT | [RL-22](http://web.mit.edu/dimitrib/www/RLbook.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLmH30BG15SIoXhxLldoio0BhsIY84YMDj) | S2022 |\n| | | | | | |\n\n[Go to Contents :arrow_heading_up:](https://github.com/kmario23/deep-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/Instructor(s) | Course WebPage | Lecture Videos | Year |\n| ---- | ------------------------------------------------------------ | --------------------------------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------- |\n| 1. | **Probabilistic Graphical Models** | Many Legends, MPI-IS | [MLSS-Tuebingen](http://mlss.tuebingen.mpg.de/2013/2013/speakers.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLL0GjJzXhAWTRiW_ynFswMaiLSa0hjCZ3) | 2013 |\n| 2. | **Probabilistic Modeling and Machine Learning** | Zoubin Ghahramani, University of Cambridge | [WUST-Wroclaw](https://www.ii.pwr.edu.pl/~gonczarek/zoubin.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLwUOK5j_XOsdfVAGKErx9HqnrVZIuRbZ2) | 2013 |\n| 3. | **Probabilistic Graphical Models** | Eric Xing, CMU | [10-708](http://www.cs.cmu.edu/~epxing/Class/10708/lecture.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLI3nIOD-p5aoXrOzTd1P6CcLavu9rNtC-) | 2014 |\n| 4. | **Learning with Structured Data: An Introduction to Probabilistic Graphical Models** | Christoph Lampert, IST Austria | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLEqoHzpnmTfA0wc1JxjoVVOrJlx8W0rGf) | 2016 |\n| 5. | **Probabilistic Graphical Models** | Nicholas Zabaras, University of Notre Dame | [PGM](https://www.zabaras.com/probabilistic-graphical-models) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLd-PuDzW85AcV4bgdu7wHPL37hm60W4RM) | 2018 |\n| 6. | **Probabilistic Graphical Models** | Eric Xing, CMU | [10-708](https://sailinglab.github.io/pgm-spring-2019/) | [Lecture-Videos](https://sailinglab.github.io/pgm-spring-2019/lectures) \u003cbr\u003e [YouTube-Lectures](https://www.youtube.com/playlist?list=PLoZgVqqHOumTY2CAQHL45tQp6kmDnDcqn) | S2019 |\n| 7. | **Probabilistic Graphical Models** | Eric Xing, CMU | [10-708](https://www.cs.cmu.edu/~epxing/Class/10708-20/index.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLoZgVqqHOumTqxIhcdcpOAJOOimrRCGZn) | S2020 |\n| 8. | **Uncertainty Modeling in AI** | Gim Hee Lee, National University of Singapura (NUS) | [CS 5340 - CH](https://www.coursehero.com/sitemap/schools/2652-National-University-of-Singapore/courses/7821096-CS5340/), [CS 5340-NB](https://github.com/clear-nus/CS5340-notebooks) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLxg0CGqViygOb9Eyc8IXM27doxjp2SK0H) | 2020-21 |\n| | | | | | |\n\n[Go to Contents :arrow_heading_up:](https://github.com/kmario23/deep-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/Instructor(s) | Course WebPage | Lecture Videos | Year |\n| ---- | --------------------------------------------------- | --------------------------------- | -------------------------------------------------------- | ------------------------------------------------------------ | -------- |\n| 1. | **Bayesian Neural Networks, Variational Inference** | Lots of Legends | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLM4Pv4KYYzGwUB4bFy183hwGhpL9ytvA1) | 2014-now |\n| 2. | **Variational Inference** | Chieh Wu, Northeastern University | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLdk2fd27CQzSd1sQ3kBYL4vtv6GjXvPsE) | 2015 |\n| 3. | **Deep Learning and Bayesian Methods** | Lots of Legends, HSE Moscow | [DLBM-SS](http://deepbayes.ru/2018) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLe5rNUydzV9Q01vWCP9BV7NhJG3j7mz62) | 2018 |\n| 4. | **Deep Learning and Bayesian Methods** | Lots of Legends, HSE Moscow | [DLBM-SS](http://deepbayes.ru/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLe5rNUydzV9QHe8VDStpU0o8Yp63OecdW) | 2019 |\n| 5. | **Nordic Probabilistic AI** | Lots of Legends, NTNU, Trondheim | [ProbAI](https://github.com/probabilisticai/probai-2019) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLRy-VW__9hV8s--JkHXZvnd26KgjRP2ik) | 2019 |\n| | | | | | |\n\n[Go to Contents :arrow_heading_up:](https://github.com/kmario23/deep-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/Instructor(s) | Course WebPage | Lecture Videos | Year |\n| ---- | ------------------------------------------------------------ | ------------------------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ----- |\n| 1. | **Medical Imaging Summer School** | Lots of Legends, Sicily | [MISS-14](http://iplab.dmi.unict.it/miss14/programme.html) | [YouTube-","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkmario23%2Fdeep-learning-drizzle","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fkmario23%2Fdeep-learning-drizzle","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkmario23%2Fdeep-learning-drizzle/lists"}