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https://github.com/kmario23/deep-learning-drizzle
Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!
https://github.com/kmario23/deep-learning-drizzle
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
Last synced: 21 days ago
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Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!
- Host: GitHub
- URL: https://github.com/kmario23/deep-learning-drizzle
- Owner: kmario23
- Created: 2018-11-26T01:17:36.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2024-02-02T09:35:52.000Z (9 months ago)
- Last Synced: 2024-10-01T19:23:43.361Z (about 1 month ago)
- 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
- Language: HTML
- Homepage: https://deep-learning-drizzle.github.io
- Size: 441 KB
- Stars: 12,145
- Watchers: 607
- Forks: 2,917
- Open Issues: 6
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome - deep-learning-drizzle - Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!! (Others)
- SecondaryAwesomeCollection - kmario23/deep-learning-drizzle
- 100-AI-Machine-learning-Deep-learning-Computer-vision-NLP - 👆
README
# :balloon: :tada: Deep Learning Drizzle :confetti_ball: :balloon:
: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: ​
Prof. Geoffrey Hinton, University of Toronto
:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:
### Contents
:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:
| | |
| ------------------------------------------------------------ | ------------------------------------------------------------ |
| **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) |
| | |
| **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) |
| | |
| **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) |
| | |
| **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) |
| | |
| **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) |
| | |
| **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) |
| | |
| **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) |
| | |
:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:
## :tada: Deep Learning (Deep Neural Networks) :confetti_ball: :balloon:
:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:
| S.No | Course Name | University/Instructor(s) | Course WebPage | Lecture Videos | Year |
| ---- | ----------------------------------------------------- | ---------------------------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | --------------- |
| 1. | **Neural Networks for Machine Learning** | Geoffrey Hinton, University of Toronto | [Lecture-Slides](http://www.cs.toronto.edu/~hinton/coursera_slides.html)
[CSC321-tijmen](https://www.cs.toronto.edu/~tijmen/csc321/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLoRl3Ht4JOcdU872GhiYWf6jwrk_SNhz9)
[UofT-mirror](https://www.cs.toronto.edu/~hinton/coursera_lectures.html) | 2012
2014 |
| 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 |
| 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 |
| 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 |
| 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 |
| 6. | **CS231n: CNNs for Visual Recognition** | Andrej Karpathy, Stanford University | [CS231n](http://cs231n.stanford.edu/2015/) | `None` | 2015 |
| 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 |
| 8. | **Bay Area Deep Learning** | Many legends, Stanford | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLrAXtmErZgOfMuxkACrYnD2fTgbzk2THW) | 2016 |
| 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)
[(Academic Torrent)](https://academictorrents.com/details/46c5af9e2075d9af06f280b55b65cf9b44eb9fe7) | 2016 |
| 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)
[(Academic Torrent)](https://academictorrents.com/details/e046bca3bc837053d1609ef33d623ee5c5af7300) | 2016 |
| | | | | | |
| 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)
[(Academic Torrent)](https://academictorrents.com/details/dd9b74b50a1292b4b154094b7338ec1d66e8894d) | 2016 |
| 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 |
| 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)
[(Academic Torrent)](https://academictorrents.com/details/ed8a16ebb346e14119a03371665306609e485f13) | 2017 |
| 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 |
| 15. | **Deep Learning Crash Course** | Leo Isikdogan, UT Austin | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLWKotBjTDoLj3rXBL-nEIPRN9V3a9Cx07) | 2017 |
| 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 |
| 17. | **Deep Learning** | Andrew Ng, Stanford University | [CS230](http://cs230.stanford.edu/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLoROMvodv4rOABXSygHTsbvUz4G_YQhOb) | 2018 |
| 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 |
| 19. | **Advanced Deep Learning and Reinforcement Learning** | Many legends, DeepMind | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLqYmG7hTraZDNJre23vqCGIVpfZ_K2RZs) | 2018 |
| 20. | **Machine Learning** | Peter Bloem, Vrije Universiteit Amsterdam | [MLVU](https://mlvu.github.io/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLCof9EqayQgsORO3pFzeYZFz6cszYO0VJ) | 2018 |
| | | | | | |
| 21. | **Deep Learning** | Francois Fleuret, EPFL | [EE-59](https://fleuret.org/ee559-2018/dlc) | [Video-Lectures](https://fleuret.org/ee559-2018/dlc/#materials) | 2018 |
| 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)
[2017-version](https://www.youtube.com/playlist?list=PLkkuNyzb8LmxFutYuPA7B4oiMn6cjD6Rs) | 2017- 2021 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 29. | **Deep Learning for AI** | UPC Barcelona | [DLAI-2017](https://telecombcn-dl.github.io/2017-dlai/)
[DLAI-2018](https://telecombcn-dl.github.io/2018-dlai/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL-5eMc3HQTBagIUjKefjcTbnXC0wXC_vd) | 2017-2018 |
| 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 |
| | | | | | |
| 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 |
| 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 |
| 33. | **Theories of Deep Learning** | Many Legends, Stanford | [Stats-385](https://stats385.github.io/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLwUqqMt5en7fFLwSDa9V3JIkDam-WWgqy)
(first 10 lectures) | F2017 |
| 34. | **Neural Networks** | Grant Sanderson | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi) | 2017-2018 |
| 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 |
| 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 |
| 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 |
| 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 |
| 39. | **Machine Learning** | Peter Bloem, Vrije Universiteit Amsterdam | [MLVU](https://mlvu.github.io/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLCof9EqayQgupldnTvqNy_BThTcME5r93) | 2019 |
| 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 |
| | | | | | |
| 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 |
| 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)
[Recitations](https://www.youtube.com/playlist?list=PLp-0K3kfddPxf4T59JEQKv5UanLPVsxzz) | F2019 |
| 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 |
| 44. | **Deep Learning** | Prabir Kumar Biswas, IIT Kgp | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLbRMhDVUMngc7NM-gDwcBzIYZNFSK2N1a) | 2019 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| | | | | | |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 57. | **Machine Learning** | Peter Bloem, Vrije Universiteit Amsterdam | [VUML](https://mlvu.github.io/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLCof9EqayQgthR7IViXkAkUwel_rhxGYM) | S2020 |
| 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 |
| 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 |
| 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)
[Lecture-Videos](https://www.video.uni-erlangen.de/course/id/925) | SS2020 |
| | | | | | |
| 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 |
| 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)
[YouTube-Lectures-P2](https://www.youtube.com/channel/UCUm7yUmVJyAbYh_0ppJ4H-g/videos) | 2020 |
| 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 |
| 64. | **MultiModal Machine Learning** | Louis-Philippe Morency & 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| | | | | | |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 76. | **Applied Deep Learning** | Alexander Pacha, TU Wien | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLNsFwZQ_pkE8xNYTEyorbaWPN7nvbWyk1) | 2020-2021 |
| 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 |
| 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 |
| 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 |
| 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 |
| | | | | | |
| 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 |
| | | | | | |
[Go to Contents :arrow_heading_up:](https://github.com/kmario23/deep-learning-drizzle#contents)
:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:
### :cupid: Machine Learning Fundamentals :cyclone: :boom:
:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:
| S.No | Course Name | University/Instructor(s) | Course Webpage | Video Lectures | Year |
| ---- | ------------------------------------------------------------ | ------------------------------------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ---------- |
| 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 |
| 2. | **Probability Primer** | Jeffrey Miller, Brown University | `mathematical monk` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL17567A1A3F5DB5E4) | 2011 |
| 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 |
| 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 |
| 5. | **Probability and Statistics** | Michel van Biezen | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLX2gX-ftPVXUWwTzAkOhBdhplvz0fByqV) | 2015 |
| 6. | **Linear Algebra: An in-depth Introduction** | Pavel Grinfeld | `None` | [Part-1](https://www.youtube.com/playlist?list=PLlXfTHzgMRUKXD88IdzS14F4NxAZudSmv)
[Part-2](https://www.youtube.com/playlist?list=PLlXfTHzgMRULWJYthculb2QWEiZOkwTSU)
[Part-3](https://www.youtube.com/playlist?list=PLlXfTHzgMRUIqYrutsFXCOmiqKUgOgGJ5)
[Part-4](https://www.youtube.com/playlist?list=PLlXfTHzgMRULZfrNCrrJ7xDcTjGr633mm) | 2015- 2017 |
| 7. | **Multivariable Calculus** | Grant Sanderson, Khan Academy | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLSQl0a2vh4HC5feHa6Rc5c0wbRTx56nF7) | 2016 |
| 8. | **Essence of Linear Algebra** | Grant Sanderson | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab) | 2016 |
| 9. | **Essence of Calculus** | Grant Sanderson | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLZHQObOWTQDMsr9K-rj53DwVRMYO3t5Yr) | 2017-2018 |
| 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 |
| | | | | | |
| 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 |
| 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 |
| 13. | **Engineering Probability** | Rich Radke, Rensselaer Polytechnic Institute | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLuh62Q4Sv7BU1dN2G6ncyiMbML7OXh_Jx) | 2018 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 19. | **Essential Statistics for Neuroscientists** | Philipp Berens, Universität Klinikum Tübingen | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL05umP7R6ij0Gw5SLIrOA1dMYScCx4oXT) | 2020 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| | | | | | |
| | | | | | |
[Go to Contents :arrow_heading_up:](https://github.com/kmario23/deep-learning-drizzle#contents)
:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:
### :cupid: Optimization for Machine Learning :cyclone: :boom:
:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:
| S.No | Course Name | University/Instructor(s) | Course Webpage | Video Lectures | Year |
| ---- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | ---------- |
| 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 |
| 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 |
| 3. | **Optimization for Machine Learning** | S V N Vishwanathan, Purdue University | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL09B0E8AFC69BE108) | 2011 |
| 4. | **Optimization** | Geoff Gordon & Ryan Tibshirani, CMU | [10-725](https://www.cs.cmu.edu/~ggordon/10725-F12/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL7y-1rk2cCsDOv91McLOnV4kExFfTB7dU) | 2012 |
| 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 |
| 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 |
| 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 |
| 8. | **Numerical Optimization** | Shirish K. Shevade, IISC | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL6EA0722B99332589) | 2015 |
| 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 |
| 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 |
| 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 |
| 12. | **Introduction to Optimization** | Michael Zibulevsky, Technion | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLBD31626529B0AC2A) | 2016 |
| 13. | **Convex Optimization** | Javier Peña & 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 |
| 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)
[Lecture-Videos](https://www.stat.cmu.edu/~ryantibs/convexopt-F18/) | F2018 |
| 15. | **Modern Algorithmic Optimization** | Yurii Nesterov, UCLouvain | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLEqoHzpnmTfAoUDqnmMly-KgyJ6ZM_axf) | 2018 |
| 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)
[YouTube-Lectures-Opt](https://www.youtube.com/playlist?list=PLcjqUUQt__ZE0ZSTNRyBIgLJ5obPHdmxC) | 2019 - now |
| 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 |
| 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 |
| 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 |
| 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- |
| | | | | | |
| 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 |
| 22. | **Combinatorial Optimization** | Constantine Caramanis, UT Austin | [comb-op](https://caramanis.github.io/teaching/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLXsmhnDvpjORcTRFMVF3aUgyYlHsxfhNL) | F2020 |
| 23. | **Optimization Methods for Machine Learning and Engineering** | Julius Pfrommer, Jürgen Beyerer, Karlsruher Institut für Technologie (KIT) | [Optim-MLE](https://ies.anthropomatik.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 |
| | | | | | |
[Go to Contents :arrow_heading_up:](https://github.com/kmario23/deep-learning-drizzle#contents)
:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:
### :cupid: General Machine Learning :cyclone: :boom:
:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:
| S.No | Course Name | University/Instructor(s) | Course Webpage | Video Lectures | Year |
| ---- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | --------- |
| 1. | **CS229: Machine Learning** | Andrew Ng, Stanford University | [CS229-old](https://see.stanford.edu/Course/CS229/)
[CS229-new](http://cs229.stanford.edu/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLA89DCFA6ADACE599) | 2007 |
| 2. | **Machine Learning** | Jeffrey Miller, Brown University | `mathematical monk` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLD0F06AA0D2E8FFBA) | 2011 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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)
[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 |
| | | | | | |
| 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 |
| 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 |
| 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 |
| 14. | **Machine Learning Theory** | Shai Ben-David, University of Waterloo | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLPW2keNyw-usgvmR7FTQ3ZRjfLs5jT4BO) | 2015 |
| 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 |
| 16. | **Statistical Machine Learning** | Larry Wasserman, CMU | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLjbUi5mgii6BWEUZf7He6nowWvGne_Y8r) | S2015 |
| 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 |
| 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 |
| 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 |
| 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 |
| | | | | | |
| 21. | **Statistical Machine Learning** | Larry Wasserman, CMU | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLTB9VQq8WiaCBK2XrtYn5t9uuPdsNm7YE) | S2016 |
| 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 |
| 23. | **Math Background for Machine Learning** | Geoffrey Gordon, CMU | `10-600` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL7y-1rk2cCsA339crwXMWUaBRuLBvPBCg) | F2016 |
| 24. | **Statistical Learning - Classification** | Ali Ghodsi, University of Waterloo | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLehuLRPyt1HzXDemu7K4ETcF0Ld_B5adG) | 2017 |
| 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 |
| 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 |
| 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 |
| 28. | **Machine Learning for Computer Vision** | Fred Hamprecht, Heidelberg University | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLuRaSnb3n4kSQFyt8VBldsQ9pO9Xtu8rY) | F2017 |
| 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 |
| 30. | **Data Visualization** | Ali Ghodsi, University of Waterloo | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLehuLRPyt1HzQoXEhtNuYTmd0aNQvtyAK) | 2017 |
| | | | | | |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| | | | | | |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 47. | **Machine Learning for Physicists** | Florian Marquardt, Uni Erlangen-Nürnberg | `None` | [Lecture-Videos](https://www.video.uni-erlangen.de/course/id/778) | 2019 |
| 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 |
| 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 |
| 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 |
| | | | | | |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 59. | **Introduction to Machine Learning** | Dmitry Kobak, Universität Klinikum Tübingen | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL05umP7R6ij35ShKLDqccJSDntugY4FQT) | 2020 |
| 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 |
| | | | | | |
| 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 |
| 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 |
| 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 |
| | | | | | |
[Go to Contents :arrow_heading_up:](https://github.com/kmario23/deep-learning-drizzle#contents)
:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:
### :balloon: Reinforcement Learning :hotsprings: :video_game:
:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:
| S.No | Course Name | University/Instructor(s) | Course Webpage | Video Lectures | Year |
| ---- | -------------------------------------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | ------ |
| 1. | **A Short Course on Reinforcement Learning** | Satinder Singh, UMichigan | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLM4Pv4KYYzGy4cIFQ5C36-1jMNLab80Ky) | 2011 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| | | | | | |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 - |
| 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 |
| | | | | | |
| 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 |
| 22. | **Reinforcement Learning** | Antonio Celani and Emanuele Panizon, International Centre for Theoretical Physics | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLp0hSY2uBeP8q2G3mfHGVGvQFEMX0QRWM) | 2021 |
| 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 |
| 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 |
| 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 |
| 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 |
| 27. | **Reinforcement Learning Lecture Series** | Lots of Legends, DeepMind & UC London | [RL-series](https://deepmind.com/learning-resources/reinforcement-learning-series-2021) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLqYmG7hTraZDVH599EItlEWsUOsJbAodm) | 2021 |
| 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 |
| | | | | | |
[Go to Contents :arrow_heading_up:](https://github.com/kmario23/deep-learning-drizzle#contents)
:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:
### :loudspeaker: Probabilistic Graphical Models :sparkles:
:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:
| S.No | Course Name | University/Instructor(s) | Course WebPage | Lecture Videos | Year |
| ---- | ------------------------------------------------------------ | --------------------------------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------- |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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)
[YouTube-Lectures](https://www.youtube.com/playlist?list=PLoZgVqqHOumTY2CAQHL45tQp6kmDnDcqn) | S2019 |
| 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 |
| 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 |
| | | | | | |
[Go to Contents :arrow_heading_up:](https://github.com/kmario23/deep-learning-drizzle#contents)
:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:
## :game_die: Bayesian Deep Learning :spades: :gem:
:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:
| S.No | Course Name | University/Instructor(s) | Course WebPage | Lecture Videos | Year |
| ---- | --------------------------------------------------- | --------------------------------- | -------------------------------------------------------- | ------------------------------------------------------------ | -------- |
| 1. | **Bayesian Neural Networks, Variational Inference** | Lots of Legends | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLM4Pv4KYYzGwUB4bFy183hwGhpL9ytvA1) | 2014-now |
| 2. | **Variational Inference** | Chieh Wu, Northeastern University | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLdk2fd27CQzSd1sQ3kBYL4vtv6GjXvPsE) | 2015 |
| 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 |
| 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 |
| 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 |
| | | | | | |
[Go to Contents :arrow_heading_up:](https://github.com/kmario23/deep-learning-drizzle#contents)
:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:
## :movie_camera: Medical Imaging :camera: :video_camera:
:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:
| S.No | Course Name | University/Instructor(s) | Course WebPage | Lecture Videos | Year |
| ---- | ------------------------------------------------------------ | ------------------------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ----- |
| 1. | **Medical Imaging Summer School** | Lots of Legends, Sicily | [MISS-14](http://iplab.dmi.unict.it/miss14/programme.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL_VeUGLULXQtvcCdAgmvKoJ1k0Ajhz-Qu) | 2014 |
| 2. | **Biomedical Image Analysis Summer School** | Lots of Legends, Paris | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLgSHH6boFf5uJAUT4ZRiAZc_ofXolkAGK) | 2015 |
| 3. | **Medical Imaging Summer School** | Lots of Legends, Sicily | [MISS-16](http://iplab.dmi.unict.it/miss16/programme.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLTRCr47yTx5iXIYSneX3LKf16upaw59wa) | 2016 |
| 4. | **OPtical and UltraSound imaging - OPUS** | Lots of Legends, Université de Lyon, France | [OPUS'16](https://opus2016lyon.sciencesconf.org/resource/page/id/2) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL95ayoVLX8GdUKbxu-R9WqRWwzdWcKjti) | 2016 |
| 5. | **Medical Imaging Summer School** | Lots of Legends, Sicily | [MISS-18](http://iplab.dmi.unict.it/miss/programme.htm) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL_VeUGLULXQux1dV4iA3XuMX6AueJmGGa) | 2018 |
| 6. | **Seminar on AI in Healthcare** | Lots of Legends, Stanford | [CS 522](http://cs522.stanford.edu/2018/index.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLYn-ZmPR1DtNQJ-ot-L2V2EgUEH6OH_7w) | 2018 |
| 7. | **Machine Learning for Healthcare** | David Sontag, Peter Szolovits, CSAIL MIT | [MLHC-19](https://mlhc19mit.github.io/)
[MIT 6.S897](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-s897-machine-learning-for-healthcare-spring-2019/lecture-notes/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLUl4u3cNGP60B0PQXVQyGNdCyCTDU1Q5j) | S2019 |
| 8. | **Deep Learning and Medical Applications** | Lots of Legends, IPAM, UCLA | [DLM-20](https://www.ipam.ucla.edu/programs/workshops/deep-learning-and-medical-applications/?tab=schedule) | [Lecture-Videos](https://www.ipam.ucla.edu/programs/workshops/deep-learning-and-medical-applications/?tab=schedule) | 2020 |
| 9. | **Stanford Symposium on Artificial Intelligence in Medicine and Imaging** | Lots of Legends, Stanford AIMI | [AIMI-20](https://aimi.stanford.edu/news-events/aimi-symposium/agenda) | [YouTube-Lectures](https://www.youtube.com/watch?v=tR2ObiL4il8&list=PLe6zdIMe5B7IR0oDOobXBDBlYY1eqLYPx) | 2020 |
| | | | | | |
[Go to Contents :arrow_heading_up:](https://github.com/kmario23/deep-learning-drizzle#contents)
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## :tada: Graph Neural Networks (Geometric DL) :confetti_ball: :balloon:
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| S.No | Course Name | University/Instructor(s) | Course WebPage | Lecture Videos | Year |
| ---- | ------------------------------------------------------------ | ------------------------------------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ----- |
| 1. | **Deep learning on graphs and manifolds** | Michael Bronstein, Technion | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLH39kM3nuavcVOUIIBraBNHjv-CwEd1uV) | 2017 |
| 2. | **Geometric Deep Learning on Graphs and Manifolds** | Michael Bronstein, Technische Universität München | `None` | [Lec-part1](https://streams.tum.de/Mediasite/Play/1f3b894e78f6400daa7885c886b936fb1d),
[Lec-part2](https://streams.tum.de/Mediasite/Play/6039c846b2f84e7a806024c06e3f5c5c1d) | 2017 |
| 3. | **Eurographics Symposium on Geometry Processing - Graduate School** | Lots of Legends, SIGGRAPH, London | [SGP-2017](http://geometry.cs.ucl.ac.uk/SGP2017/?p=gradschool) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLOp-ngXvomHArqntgLVNzuJNdzNx3rDjZ) | 2017 |
| 4. | **Eurographics Symposium on Geometry Processing - Graduate School** | Lots of Legends, SIGGRAPH, Paris | [SGP-2018](https://sgp2018.sciencesconf.org/resource/page/id/7) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLvcoRb-DvAmgpp8LYw7dUvLxh-1Vrrm-v) | 2018 |
| 5. | **Analysis of Networks: Mining and Learning with Graphs** | Jure Leskovec, Stanford University | [CS224W](http://snap.stanford.edu/class/cs224w-2018/) | [Lecture-Videos](http://snap.stanford.edu/class/cs224w-2018/) | 2018 |
| 6. | **Machine Learning with Graphs** | Jure Leskovec, Stanford University | [CS224W](http://snap.stanford.edu/class/cs224w-2019/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL-Y8zK4dwCrQyASidb2mjj_itW2-YYx6-) | 2019 |
| 7. | Geometry and Learning from Data in 3D and Beyond -**Geometry and Learning from Data Tutorials** | Lots of Legends, IPAM UCLA | [GLDT](http://www.ipam.ucla.edu/programs/workshops/geometry-and-learning-from-data-tutorials) | [Lecture-Videos](http://www.ipam.ucla.edu/programs/workshops/geometry-and-learning-from-data-tutorials/?tab=schedule) | 2019 |
| 8. | Geometry and Learning from Data in 3D and Beyond - **Geometric Processing** | Lots of Legends, IPAM UCLA | [GeoPro](http://www.ipam.ucla.edu/programs/workshops/workshop-i-geometric-processing/) | [Lecture-Videos](http://www.ipam.ucla.edu/programs/workshops/workshop-i-geometric-processing/?tab=schedule) | 2019 |
| 9. | Geometry and Learning from Data in 3D and Beyond - **Shape Analysis** | Lots of Legends, IPAM UCLA | [Shape-Analysis](http://www.ipam.ucla.edu/programs/workshops/workshop-ii-shape-analysis/) | [Lecture-Videos](http://www.ipam.ucla.edu/programs/workshops/workshop-ii-shape-analysis/?tab=schedule) | 2019 |
| 10. | Geometry and Learning from Data in 3D and Beyond - **Geometry of Big Data** | Lots of Legends, IPAM UCLA | [Geo-BData](http://www.ipam.ucla.edu/programs/workshops/workshop-iii-geometry-of-big-data) | [Lecture-Videos](http://www.ipam.ucla.edu/programs/workshops/workshop-iii-geometry-of-big-data/?tab=schedule) | 2019 |
| | | | | | |
| 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://www.ipam.ucla.edu/programs/workshops/workshop-iv-deep-geometric-learning-of-big-data-and-applications) | [Lecture-Videos](http://www.ipam.ucla.edu/programs/workshops/workshop-iv-deep-geometric-learning-of-big-data-and-applications/?tab=schedule) | 2019 |
| 12. | **Israeli Geometric Deep Learning** | Lots of Legends, Israel | [iGDL-20](https://gdl-israel.github.io/schedule.html) | [Lecture-Videos](https://www.youtube.com/watch?v=c8_32IVn-sg) | 2020 |
| 13. | **Machine Learning for Graphs and Sequential Data** | Stephan Günnemann, Technische Universität München (TUM) | [MLGS-20](https://www.in.tum.de/en/daml/teaching/summer-term-2020/machine-learning-for-graphs-and-sequential-data/) | [Lecture-Videos](https://www.in.tum.de/daml/teaching/mlgs/) | S2020 |
| 14. | **Machine Learning with Graphs** | Jure Leskovec, Stanford | [CS224W](http://web.stanford.edu/class/cs224w) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLoROMvodv4rPLKxIpqhjhPgdQy7imNkDn) | W2021 |
| 15. | **Geometric Deep Learning** - AMMI | Lots of Legends, Virtual | [GDL-AMMI](https://geometricdeeplearning.com/lectures) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLn2-dEmQeTfQ8YVuHBOvAhUlnIPYxkeu3) | 2021 |
| 16. | **Summer School on Geometric Deep Learning** - | Lots of Legends, DTU, DIKU & AAU | [GDL- DTU, DIKU & AAU](https://geometric-deep-learning.compute.dtu.dk) | [Lecture-Videos](https://geometric-deep-learning.compute.dtu.dk/talks-and-materials) | 2021 |
| 17. | **Graph Neural Networks** | Alejandro Ribeiro, University of Pennsylvania | [ESE 514](https://gnn.seas.upenn.edu) | [YouTube-Lectures](https://www.youtube.com/channel/UC_YPrqpiEqkeGOG1TCt0giQ/playlists) | F2021 |
| | | | | | |
[Go to Contents :arrow_heading_up:](https://github.com/kmario23/deep-learning-drizzle#contents)
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### :hibiscus: Natural Language Processing :cherry_blossom: :sparkling_heart:
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| S.No | Course Name | University/Instructor(s) | Course WebPage | Lecture Videos | Year |
| ---- | --------------------------------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | --------- |
| 1. | **Computational Linguistics I** | Jordan Boyd-Graber, University of Maryland | [CMS-723](http://users.umiacs.umd.edu/~jbg/teaching/CMSC_723/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLegWUnz91WfuPebLI97-WueAP90JO-15i) | 2013-2018 |
| 2. | **Deep Learning for Natural Language Processing** | Nils Reimers, TU Darmstadt | [DL4NLP](https://github.com/UKPLab/deeplearning4nlp-tutorial) | [YouTube-Lectures](https://www.youtube.com/channel/UC1zCuTrfpjT6Sv2kJk-JkvA/videos) | 2015-2017 |
| 3. | **Deep Learning for Natural Language Processing** | Many Legends, DeepMind-Oxford | [DL-NLP](http://www.cs.ox.ac.uk/teaching/courses/2016-2017/dl/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL613dYIGMXoZBtZhbyiBqb0QtgK6oJbpm) | 2017 |
| 4. | **Deep Learning for Speech & Language** | UPC Barcelona | [DL-SL](https://telecombcn-dl.github.io/2017-dlsl/) | [Lecture-Videos](https://telecombcn-dl.github.io/2017-dlsl/) | 2017 |
| 5. | **Neural Networks for Natural Language Processing** | Graham Neubig, CMU | [NN4NLP](http://www.phontron.com/class/nn4nlp2017/) [Code](https://github.com/neubig/nn4nlp-code) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL8PYTP1V4I8ABXzdqtOpB_eqBlVAz_xPT) | 2017 |
| 6. | **Neural Networks for Natural Language Processing** | Graham Neubig, CMU | [NN4-NLP](http://www.phontron.com/class/nn4nlp2018/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL8PYTP1V4I8Ba7-rY4FoB4-jfuJ7VDKEE) | 2018 |
| 7. | **Deep Learning for NLP** | Min-Yen Kan, NUS | [CS-6101](https://www.comp.nus.edu.sg/~kanmy/courses/6101_1810/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLllwxvcS7ca5eD44KTCiT7Rmu_hFAafXB) | 2018 |
| 8. | **Neural Networks for Natural Language Processing** | Graham Neubig, CMU | [NN4NLP](http://www.phontron.com/class/nn4nlp2019/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL8PYTP1V4I8Ajj7sY6sdtmjgkt7eo2VMs) | 2019 |
| 9. | **Natural Language Processing with Deep Learning** | Abigail See, Chris Manning, Richard Socher, Stanford University | [CS224n](http://web.stanford.edu/class/cs224n/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLoROMvodv4rOhcuXMZkNm7j3fVwBBY42z) | 2019 |
| 10. | **Natural Language Understanding** | Bill MacCartney and Christopher Potts | [CS224U](https://web.stanford.edu/class/cs224u) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLoROMvodv4rObpMCir6rNNUlFAn56Js20) | S2019 |
| | | | | | |
| 11. | **Neural Networks for Natural Language Processing** | Graham Neubig, Carnegie Mellon University | [CS 11-747](http://www.phontron.com/class/nn4nlp2020/schedule.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL8PYTP1V4I8CJ7nMxMC8aXv8WqKYwj-aJ) | S2020 |
| 12. | **Advanced Natural Language Processing** | Mohit Iyyer, UMass Amherst | [CS 685](https://people.cs.umass.edu/~miyyer/cs685) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLWnsVgP6CzadmQX6qevbar3_vDBioWHJL) | F2020 |
| 13. | **Machine Translation** | Philipp Koehn, Johns Hopkins University | [EN 601.468/668](http://mt-class.org/jhu/syllabus.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLQrCiUDqDLG0lQX54o9jB4phJ-SLI6ZBQ) | F2020 |
| 14. | **Neural Networks for NLP** | Graham Neubig, Carnegie Mellon University | [CS 11-747](http://www.phontron.com/class/nn4nlp2021) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL8PYTP1V4I8AkaHEJ7lOOrlex-pcxS-XV) | 2021 |
| 15. | **Deep Learning for Natural Language Processing** | Kyunghyun Cho, New York University | [DS-GA 1011](https://drive.google.com/drive/folders/1ykXBtophaY_65VHK_8yDzZQJwfJDD5Ve) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLdH9u0f1XKW_s-c8EcgJpn_HJz5Jj1IRf) | F2021 |
| 16. | **Natural Language Processing with Deep Learning** | Chris Manning, Stanford University | [CS224n](https://web.stanford.edu/class/archive/cs/cs224n/cs224n.1214/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLoROMvodv4rOSH4v6133s9LFPRHjEmbmJ) | 2021 |
| | | | | | |
[Go to Contents :arrow_heading_up:](https://github.com/kmario23/deep-learning-drizzle#contents)
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### :speaking_head: Automatic Speech Recognition :speech_balloon: :thought_balloon:
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| S.No | Course Name | University/Instructor(s) | Course WebPage | Lecture Videos | Year |
| ---- | ---------------------------------------- | ------------------------------ | --------------------------------------------------- | ------------------------------------------------------------ | --------- |
| 1. | **Deep Learning for Speech & Language** | UPC Barcelona | [DL-SL](https://telecombcn-dl.github.io/2017-dlsl/) | [Lecture-Videos](https://telecombcn-dl.github.io/2017-dlsl/)
[YouTube-Videos](https://www.youtube.com/playlist?list=PL-5DCZHuHZkWeF9ljIjoC_X5gHRLNtIkU) | 2017 |
| 2. | **Speech and Audio in the Northeast** | Many Legends, Google NYC | [SANE-15](http://www.saneworkshop.org/sane2015/) | [YouTube-Videos](https://www.youtube.com/playlist?list=PLBJWRPcgwk7sZOB4UTVilWWnRg84L9o5i) | 2015 |
| 3. | **Automatic Speech Recognition** | Samudra Vijaya K, TIFR | `None` | [YouTube-Videos](https://www.youtube.com/channel/UCHk6uq1Cr9J3k5KNmIsYUNw/videos) | 2016 |
| 4. | **Speech and Audio in the Northeast** | Many Legends, Google NYC | [SANE-17](http://www.saneworkshop.org/sane2017/) | [YouTube-Videos](https://www.youtube.com/playlist?list=PLBJWRPcgwk7tNLaBVu_S90ZQSblO3bwjg) | 2017 |
| 5. | **Speech and Audio in the Northeast** | Many Legends, Google Cambridge | [SANE-18](http://www.saneworkshop.org/sane2018/) | [YouTube-Videos](https://www.youtube.com/playlist?list=PLBJWRPcgwk7sjMANn8jqosyHIMe6DJhmn) | 2018 |
| | | | | | |
| -1. | **Deep Learning for Speech Recognition** | Many Legends, AoE | `None` | [YouTube-Videos](https://www.youtube.com/playlist?list=PLM4Pv4KYYzGyFYCXV6YPWAKVOR2gmHnQd) | 2015-2018 |
[Go to Contents :arrow_heading_up:](https://github.com/kmario23/deep-learning-drizzle#contents)
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### :fire: Modern Computer Vision :camera_flash: :movie_camera:
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| S.No | Course Name | University/Instructor(s) | Course WebPage | Lecture Videos | Year |
| ---- | ------------------------------------------------------------ | ------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | ---------- |
| 1. | **Microsoft Computer Vision Summer School** - (classical) | Lots of Legends, Lomonosov Moscow State University | `None` | [YouTube-Videos](https://www.youtube.com/playlist?list=PLbwKcm5vdiSYU54xFUG1zoxQTulqvIcJu)
[Russian-mirror](https://www.youtube.com/playlist?list=PL-_cKNuVAYAUp0eCL7KO8QY4ETY3tIDFH) | 2011 |
| 2. | **Computer Vision** - (classical) | Mubarak Shah, UCF | [CAP-5415](http://crcv.ucf.edu/courses/CAP5415/Fall2012/index.php) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLd3hlSJsX_Imk_BPmB_H3AQjFKZS9XgZm) | 2012 |
| 3. | **Image and Multidimensional Signal Processing** - (classical) | William Hoff, Colorado School of Mines | [CSCI 510/EENG 510](http://inside.mines.edu/~whoff/courses/EENG510) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLyED3W677ALNv8Htn0f9Xh-AHe1aZPftv) | 2012 |
| 4. | **Computer Vision** - (classical) | William Hoff, Colorado School of Mines | [CSCI 512/EENG 512](http://inside.mines.edu/~whoff/courses/EENG512/index.htm) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL4B3F8D4A5CAD8DA3) | 2012 |
| 5. | **Image and Video Processing: From Mars to Hollywood with a Stop at the Hospital** | Guillermo Sapiro, Duke University | `None` | [YouTube-Videos](https://www.youtube.com/playlist?list=PLZ9qNFMHZ-A79y1StvUUqgyL-O0fZh2rs) | 2013 |
| 6. | **Multiple View Geometry** (classical) | Daniel Cremers, Technische Universität München | [mvg](https://vision.in.tum.de/teaching/ss2014/mvg2014) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLTBdjV_4f-EJn6udZ34tht9EVIW7lbeo4) | 2013 |
| 7. | **Mathematical Methods for Robotics, Vision, and Graphics** | Justin Solomon, Stanford University | [CS-205A](http://graphics.stanford.edu/courses/cs205a/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLQ3UicqQtfNvQ_VzflHYKhAqZiTxOkSwi) | 2013 |
| 8. | **Computer Vision** - (classical) | Mubarak Shah, UCF | [CAP-5415](http://crcv.ucf.edu/courses/CAP5415/Fall2014/index.php) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLd3hlSJsX_ImKP68wfKZJVIPTd8Ie5u-9) | 2014 |
| 9. | **Computer Vision for Visual Effects** (classical) | Rich Radke, Rensselaer Polytechnic Institute | [ECSE-6969](https://www.ecse.rpi.edu/~rjradke/cvfxcourse.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLuh62Q4Sv7BUJlKlt84HFqSWfW36MDd5a) | S2014 |
| 10. | **Autonomous Navigation for Flying Robots** | Juergen Sturm, Technische Universität München | [Autonavx](https://jsturm.de/wp/teaching/autonavx-slides/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLTBdjV_4f-EKBCUs1HmMtsnXv4JUoFrzg) | 2014 |
| | | | | | |
| 11. | **SLAM - Mobile Robotics** | Cyrill Stachniss, Universitaet Freiburg | [RobotMapping](http://ais.informatik.uni-freiburg.de/teaching/ws13/mapping/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLgnQpQtFTOGQrZ4O5QzbIHgl3b1JHimN_) | 2014 |
| 12. | **Computational Photography** | Irfan Essa, David Joyner, Arpan Chakraborty | [CP-Udacity](https://eu.udacity.com/course/computational-photography--ud955) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLAwxTw4SYaPn-unAWtRMleY4peSe4OzIY) | 2015 |
| 13. | **Introduction to Digital Image Processing** | Rich Radke, Rensselaer Polytechnic Institute | [ECSE-4540](https://www.ecse.rpi.edu/~rjradke/improccourse.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLuh62Q4Sv7BUf60vkjePfcOQc8sHxmnDX) | S2015 |
| 14. | **Lectures on Digital Photography** | Marc Levoy, Stanford/Google Research | [LoDP](https://sites.google.com/site/marclevoylectures/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL7ddpXYvFXspUN0N-gObF1GXoCA-DA-7i) | 2016 |
| 15. | **Introduction to Computer Vision** (foundation) | Aaron Bobick, Irfan Essa, Arpan Chakraborty | [CV-Udacity](https://eu.udacity.com/course/introduction-to-computer-vision--ud810) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLAwxTw4SYaPnbDacyrK_kB_RUkuxQBlCm) | 2016 |
| 16. | **Computer Vision** | Syed Afaq Ali Shah, University of Western Australia | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLvqB6_mDBCdlnT84LK_NvbOqcXLlOTR8j) | 2016 |
| 17. | **Photogrammetry I & II** | Cyrill Stachniss, University of Bonn | [PG-I&II](https://www.ipb.uni-bonn.de/photogrammetry-i-ii/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLgnQpQtFTOGRsi5vzy9PiQpNWHjq-bKN1) | 2016 |
| 18. | **Deep Learning for Computer Vision** | UPC Barcelona | [DLCV-16](http://imatge-upc.github.io/telecombcn-2016-dlcv/)
[DLCV-17](https://telecombcn-dl.github.io/2017-dlcv/)
[DLCV-18](https://telecombcn-dl.github.io/2018-dlcv/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL-5eMc3HQTBbuaTFP4wsfD2Y2VqEfQcaP) | 2016-2018 |
| 19. | **Convolutional Neural Networks** | Andrew Ng, Stanford University | [DeepLearning.AI](https://www.deeplearning.ai/deep-learning-specialization/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLkDaE6sCZn6Gl29AoE31iwdVwSG-KnDzF) | 2017 |
| 20. | **Variational Methods for Computer Vision** | Daniel Cremers, Technische Universität München | [VMCV](https://vision.in.tum.de/teaching/ws2016/vmcv2016) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLTBdjV_4f-EJ7A2iIH5L5ztqqrWYjP2RI) | 2017 |
| | | | | | |
| 21. | **Winter School on Computer Vision** | Lots of Legends, Israel Institute for Advanced Studies | [WS-CV](http://www.as.huji.ac.il/cse) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLTn74Qx5mPsSniA5tt6W-o0OGYEeKScug) | 2017 |
| 22. | **Deep Learning for Visual Computing** | Debdoot Sheet, IIT-Kgp | [Nptel](https://onlinecourses.nptel.ac.in/noc18_ee08/preview) [Notebooks](https://github.com/iitkliv/dlvcnptel) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLuv3GM6-gsE1Biyakccxb3FAn4wBLyfWf) | 2018 |
| 23. | **The Ancient Secrets of Computer Vision** | Joseph Redmon, Ali Farhadi | [TASCV](https://pjreddie.com/courses/computer-vision/) ; [TASCV-UW](https://courses.cs.washington.edu/courses/cse455/18sp/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLjMXczUzEYcHvw5YYSU92WrY8IwhTuq7p) | 2018 |
| 24. | **Modern Robotics** | Kevin Lynch, Northwestern Robotics | [modern-robot](http://hades.mech.northwestern.edu/index.php/Modern_Robotics) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLggLP4f-rq02vX0OQQ5vrCxbJrzamYDfx) | 2018 |
| 25. | **Digial Image Processing** | Alex Bronstein, Technion | [CS236860](https://vistalab-technion.github.io/cs236860/info/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLM0a6Z788YAZOxUyWda9y3N_i2upIj1Ep) | 2018 |
| 26. | **Mathematics of Imaging** - Variational Methods and Optimization in Imaging | Lots of Legends, Institut Henri Poincaré | [Workshop-1](http://www.ihp.fr/sites/default/files/conf1-04_au_08_fevr-imaging2019.pdf) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL9kd4mpdvWcAzD5Aq-P1TrLLiYckrloxw) | 2019 |
| 27. | **Deep Learning for Video** | Xavier Giró, UPC Barcelona | [deepvideo](https://mcv-m6-video.github.io/deepvideo-2019/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL-5eMc3HQTBbPY-627Gornj09pZrNQgPD) | 2019 |
| 28. | **Statistical modeling for shapes and imaging** | Lots of Legends, Institut Henri Poincaré, Paris | [workshop-2](https://imaging-in-paris.github.io/semester2019/workshop2prog) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL9kd4mpdvWcAzD5Aq-P1TrLLiYckrloxw) | 2019 |
| 29. | **Imaging and machine learning** | Lots of Legends, Institut Henri Poincaré, Paris | [workshop-3](https://imaging-in-paris.github.io/semester2019/workshop3prog) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL9kd4mpdvWcAzD5Aq-P1TrLLiYckrloxw) | 2019 |
| 30. | **Computer Vision** | Jayanta Mukhopadhyay, IIT Kgp | [CV-nptel](https://nptel.ac.in/courses/106/105/106105216/) | [YouTube-Lectures](https://nptel.ac.in/courses/106/105/106105216/) | 2019 |
| | | | | | |
| 31. | **Deep Learning for Computer Vision** | Justin Johnson, UMichigan | [EECS 498-007](https://web.eecs.umich.edu/~justincj/teaching/eecs498/) | [Lecture-Videos](http://leccap.engin.umich.edu/leccap/site/jhygcph151x25gjj1f0)
[YouTube-Lectures](https://www.youtube.com/playlist?list=PL5-TkQAfAZFbzxjBHtzdVCWE0Zbhomg7r) | 2019 |
| 32. | **Sensors and State Estimation 2** | Cyrill Stachniss, University of Bonn | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLgnQpQtFTOGQh_J16IMwDlji18SWQ2PZ6) | S2020 |
| 33. | **Computer Vision III: Detection, Segmentation and Tracking** | Laura Leal-Taixé, TU München | [CV3DST](https://dvl.in.tum.de/teaching/cv3dst-ss20/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLog3nOPCjKBneGyffEktlXXMfv1OtKmCs) | S2020 |
| 34. | **Advanced Deep Learning for Computer Vision** | Laura Leal-Taixé and Matthias Nießner, TU München | [ADL4CV](https://dvl.in.tum.de/teaching/adl4cv-ss20) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLog3nOPCjKBnjhuHMIXu4ISE4Z4f2jm39) | S2020 |
| 35. | **Computer Vision: Foundations** | Fred Hamprecht, Universität Heidelberg | [CVF](https://hci.iwr.uni-heidelberg.de/ial/cvf) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLuRaSnb3n4kRAbnmiyGd77hyoGzO9wPde) | SS2020 |
| 36. | **MIT Vision Seminar** | Lots of Legends, MIT | [MIT-Vision](https://sites.google.com/view/visionseminar/past-talks) | [YouTube-Lectures](https://www.youtube.com/channel/UCLMiFkFyfcNnZs6iwYLPI9g/videos) | 2015-now |
| 37. | **TUM AI Guest Lectures** | Lots of Legends, Technische Universität München | [TUM-AI](https://niessner.github.io/TUM-AI-Lecture-Series) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLQ8Y4kIIbzy8kMlz7cRqz-BjbdyWsfLXt) | 2020 - now |
| 38. | **Seminar on 3D Geometry & Vision** | Lots of Legends, Virtual | [3DGV seminar](https://3dgv.github.io) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLZk0jtN0g8e-xVTfsiV67q8Iz1cZO_FpV) | 2020 - now |
| 39. | **Event-based Robot Vision** | Guillermo Gallego, Technische Universität Berlin | [EVIS-SS20](https://sites.google.com/view/guillermogallego/teaching/event-based-robot-vision) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL03Gm3nZjVgUFYUh3v5x8jVonjrGfcal8) | 2020 - now |
| 40. | **Deep Learning for Computer Vision** | Vineeth Balasubramanian, IIT Hyderabad | [DL-CV'20](https://onlinecourses.nptel.ac.in/noc20_cs88/preview) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLyqSpQzTE6M_PI-rIz4O1jEgffhJU9GgG) | 2020 |
| | | | | | |
| 41. | **Deep Learning for Visual Computing** | Peter Wonka, KAUST, SA | `NOne` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLMpQLEui13s2DHbw6kTTxwQma8rehlfZE) | 2020 |
| 42. | **Computer Vision** | Yogesh Rawat, University of Central Florida | [CAP5415-CV](https://www.crcv.ucf.edu/courses/cap5415-fall-2020/schedule/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLd3hlSJsX_Ikm5il1HgmDB_z62BeoikFX) | F2020 |
| 43. | **Multimedia Signal Processing** | Mark Hasegawa-Johnson, UIUC | [ECE-417 MSP](https://courses.engr.illinois.edu/ece417/fa2020/) | [Lecture Videos](https://mediaspace.illinois.edu/channel/ECE%20417/26816181) | F2020 |
| 44. | **Computer Vision** | Andreas Geiger, Universität Tübingen | [Comp.Vis](https://uni-tuebingen.de/fakultaeten/mathematisch-naturwissenschaftliche-fakultaet/fachbereiche/informatik/lehrstuehle/autonomous-vision/lectures/computer-vision/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL05umP7R6ij35L2MHGzis8AEHz7mg381_) | S2021 |
| 45. | **3D Computer Vision** | Lee Gim Hee, National Univeristy of Singapura | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLxg0CGqViygP47ERvqHw_v7FVnUovJeaz) | 2021 |
| 46. | **Deep Learning for Computer Vision: Fundamentals and Applications** | T. Dekel et al., Weizmann Institute of Science | [DL4CV](https://dl4cv.github.io/schedule.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL_Z2_U9MIJdNgFM7-f2fZ9ZxjVRP_jhJv) | S2021 |
| 47. | **Current Topics in ML Methods in 3D and Geometric Deep Learning** | Animesh Garg & others, University of Toronto | [CSC 2547](http://www.pair.toronto.edu/csc2547-w21) | [YouTube-Lectures](https://www.youtube.com/channel/UCrsmAXnwu6sgccWevW12Dfg/videos) | 2021 |
| 48. | **First Principles of Computer Vision** | Shree K. Nayar, Columbia University | [FPCV](https://fpcv.cs.columbia.edu) | [YouTube-Lectures](https://www.youtube.com/channel/UCf0WB91t8Ky6AuYcQV0CcLw/videos) | 2021 |
| 49. | **Self-Driving Cars** | Andreas Geiger, Universität Tübingen | [SDC'21](https://uni-tuebingen.de/de/123611) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL05umP7R6ij321zzKXK6XCQXAaaYjQbzr) | W2021 |
| | | | | | |
[Go to Contents :arrow_heading_up:](https://github.com/kmario23/deep-learning-drizzle#contents)
:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:
### :star2: Boot Camps or Summer Schools :maple_leaf:
:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:
| S.No | Course Name | University/Instructor(s) | Course WebPage | Lecture Videos | Year |
| ---- | ------------------------------------------------------- | -------------------------------------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | --------- |
| 1. | **Deep Learning, Feature Learning** | Lots of Legends, IPAM UCLA | [GSS-2012](https://www.ipam.ucla.edu/programs/summer-schools/graduate-summer-school-deep-learning-feature-learning/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLHyI3Fbmv0SdzMHAy0aN59oYnLy5vyyTA) | 2012 |
| 2. | **Big Data Boot Camp** | Lots of Legends, Simons Institute | [Big Data](https://simons.berkeley.edu/workshops/schedule/316) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLgKuh-lKre13RmUC2AybRvVAxO5DEMIBH) | 2013 |
| 3. | **Machine Learning Summer School** | Lots of Legends, MPI-IS Tübingen | [MLSS-13](http://mlss.tuebingen.mpg.de/2013/2013/index.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLqJm7Rc5-EXFv6RXaPZzzlzo93Hl0v91E) | 2013 |
| 4 | **Graduate Summer School: Computer Vision** | Lots of Legends, IPAM-UCLA | [GSS-CV](http://www.ipam.ucla.edu/programs/summer-schools/graduate-summer-school-computer-vision/) | [Video-Lectures](http://www.ipam.ucla.edu/programs/summer-schools/graduate-summer-school-computer-vision/?tab=schedule) | 2013 |
| 5. | **Machine Learning Summer School** | Lots of Legends, Reykjavik University | [MLSS-14](http://mlss2014.hiit.fi/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLqdbxUnkqOw2nKn7VxYqIrKWcqRkQYOsF) | 2014 |
| 6. | **Machine Learning Summer School** | Lots of Legends, Pittsburgh | [MLSS-14](http://www.mlss2014.com) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLZSO_6-bSqHQCIYxE3ycGLXHMjK3XV7Iz) | 2014 |
| 7. | **Deep Learning Summer School** | Lots of Legends, Université de Montréal | [DLSS-15](https://sites.google.com/site/deeplearningsummerschool/home) | [YouTube-Lectures](http://videolectures.net/deeplearning2015_montreal/) | 2015 |
| 8. | **Biomedical Image Analysis Summer School** | Lots of Legends, CentraleSupelec, Paris | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLgSHH6boFf5uJAUT4ZRiAZc_ofXolkAGK) | 2015 |
| 9. | **Mathematics of Signal Processing** | Lots of Legends, Hausdorff Institute for Mathematics | [SigProc](http://www.him.uni-bonn.de/signal-processing-2016/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLul8LCT3AJqSQo3lr5RbwxJ92RsgRuDtx) | 2016 |
| 10. | **Microsoft Research - Machine Learning Course** | S V N Vishwanathan and Prateek Jain MS-Research | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL34iyE0uXtxo7vPXGFkmm6KbgZQwjf9Kf) | 2016 |
| | | | | | |
| 11. | **Deep Learning Summer School** | Lots of Legends, Université de Montréal | [DL-SS-16](https://sites.google.com/site/deeplearningsummerschool2016/home) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL5bqIc6XopCbb-FvnHmD1neVlQKwGzQyR) | 2016 |
| 12. | **Lisbon Machine Learning School** | Lots of Legends, Instituto Superior Técnico, Portugal | [LxMLS-16](http://lxmls.it.pt/2016/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLToLj8M4ao-fymxXBIOU6sF1NGFLb5EiX) | 2016 |
| 13. | **Machine Learning Advances and Applications Seminar** | Lots of Legends, Fields Institute, University of Toronto | [MLAAS-16](http://www.fields.utoronto.ca/activities/16-17/machine-learning) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLfsVAYSMwskuQcRkuDApP40lX_i08d0QK)
[Video-Lectures](http://www.fields.utoronto.ca/video-archive/event/2267) | 2016-2017 |
| 14. | **Machine Learning Advances and Applications Seminar** | Lots of Legends, Fields Institute, University of Toronto | [MLAAS-17](http://www.fields.utoronto.ca/activities/17-18/machine-learning) | [Video Lectures](http://www.fields.utoronto.ca/video-archive/event/2487) | 2017-2018 |
| 15. | **Machine Learning Summer School** | Lots of Legends, MPI-IS Tübingen | [MLSS-17](http://mlss.tuebingen.mpg.de/2017/index.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLqJm7Rc5-EXFUOvoYCdKikfck8YeUCnl9) | 2017 |
| 16. | **Representation Learning** | Lots of Legends, Simons Institute | [RepLearn](https://simons.berkeley.edu/workshops/abstracts/3750) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLgKuh-lKre13UNV4ztsWUXciUZ7x_ZDHz) | 2017 |
| 17. | **Foundations of Machine Learning** | Lots of Legends, Simons Institute | [ML-BootCamp](https://simons.berkeley.edu/workshops/abstracts/3748) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLgKuh-lKre11GbZWneln-VZDLHyejO7YD) | 2017 |
| 18. | **Optimization, Statistics, and Uncertainty** | Lots of Legends, Simons Institute | [Optim-Stats](https://simons.berkeley.edu/workshops/abstracts/4795) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLgKuh-lKre13ACD44z2FH-IVP1e8ip5JO) | 2017 |
| 19. | **Deep Learning: Theory, Algorithms, and Applications** | Lots of Legends, TU-Berlin | [DL: TAA](http://doc.ml.tu-berlin.de/dlworkshop2017/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLJOzdkh8T5kqCNV_v1w2tapvtJDZYiohW) | 2017 |
| 20. | **Deep Learning and Reinforcement Learning Summer School** | Lots of Legends, Université de Montréal | [DLRL-2017](https://mila.quebec/en/cours/deep-learning-summer-school-2017/) | [Lecture-videos](http://videolectures.net/deeplearning2017_montreal/) | 2017 |
| | | | | | |
| 21. | **Statistical Physics Methods in Machine Learning** | Lots of Legends, International Centre for Theoretical Sciences, TIFR | [SPMML](https://www.icts.res.in/discussion-meeting/SPMML2017) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL04QVxpjcnjhtL3IIVyFRMOgdhWtPn7YJ) | 2017 |
| 22. | **Lisbon Machine Learning School** | Lots of Legends, Instituto Superior Técnico, Portugal | [LxMLS-17](http://lxmls.it.pt/2017/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLToLj8M4ao-fuRfnzEJCCnvuW2_FeJ73N) | 2017 |
| 23. | **Interactive Learning** | Lots of Legends, Simons Institute, Berkeley | [IL-2017](https://simons.berkeley.edu/workshops/schedule/3749) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLgKuh-lKre10T2POF-WzXh0ckdpyvANUx) | 2017 |
| 24. | **Computational Challenges in Machine Learning** | Lots of Legends, Simons Institute, Berkeley | [CCML-17](https://simons.berkeley.edu/workshops/schedule/3751) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLgKuh-lKre12eXz4dnvc8oervo2_Af4iU) | 2017 |
| 25. | **Foundations of Data Science** | Lots of Legends, Simons Institute | [DS-BootCamp](https://simons.berkeley.edu/workshops/abstracts/6680) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLgKuh-lKre13r1Qrnrejj3f498-NurSf3) | 2018 |
| 26. | **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 |
| 27. | **New Deep Learning Techniques** | Lots of Legends, IPAM UCLA | [IPAM-Workshop](https://www.ipam.ucla.edu/programs/workshops/new-deep-learning-techniques/?tab=schedule) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLHyI3Fbmv0SdM0zXj31HWjG9t9Q0v2xYN) | 2018 |
| 28. | **Deep Learning and Reinforcement Learning Summer School** | Lots of Legends, University of Toronto | [DLRL-2018](https://dlrlsummerschool.ca/2018-event/) | [Lecture-videos](http://videolectures.net/DLRLsummerschool2018_toronto/) | 2018 |
| 29. | **Machine Learning Summer School** | Lots of Legends, Universidad Autónoma de Madrid, Spain | [MLSS-18](http://mlss.ii.uam.es/mlss2018/index.html) | [YouTube-Lectures](https://www.youtube.com/channel/UCbPJHr__eIor_7jFH3HmVHQ/videos)
[Course-videos](http://mlss.ii.uam.es/mlss2018/speakers.html) | 2018 |
| 30. | **Theoretical Basis of Machine Learning** | Lots of Legends, International Centre for Theoretical Sciences, TIFR | [TBML-18](https://www.icts.res.in/discussion-meeting/tbml2018) | [Lecture-Videos](https://www.icts.res.in/discussion-meeting/tbml2018/talks)
[YouTube-Videos](https://www.youtube.com/playlist?list=PL04QVxpjcnjj1DgnXxFBo2fkSju4r-ggr) | 2018 |
| | | | | | |
| 31. | **Polish View on Machine Learning** | Lots of Legends, Warsaw | [PLinML-18](https://plinml.mimuw.edu.pl/) | [YouTube-Videos](https://www.youtube.com/playlist?list=PLoaWrlj9TDhPcA6N9dZQ6GPXboYuumDRp) | 2018 |
| 32. | **Big Data Analysis in Astronomy** | Lots of Legends, Tenerife | [BDAA-18](http://research.iac.es/winterschool/2018/pages/book-ws2018.php) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLM4Pv4KYYzGx42W5pSp3Itetp0u-PENtI) | 2018 |
| 33. | **Machine Learning Advances and Applications Seminar** | Lots of Legends, Fields Institute, University of Toronto | [MLASS](http://www.fields.utoronto.ca/activities/18-19/machine-learning) | [Video Lectures](http://www.fields.utoronto.ca/video-archive/event/2681) | 2018-2019 |
| 34. | **MIFODS- ML, Stats, ToC seminar** | Lots of Legends, MIT | [MIFODS-seminar](http://mifods.mit.edu/seminar.php) | [Lecture-videos](http://mifods.mit.edu/seminar.php) | 2018-2019 |
| 35. | **Learning Machines Seminar Series** | Lots of Legends, Cornell Tech | [LMSS](https://lmss.tech.cornell.edu/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLycW2Yy79JuxbQZ9uHEu_NS3cGNomhL2A) | 2018-now |
| 36. | **Machine Learning Summer School** | Lots of Legends, South Africa | [MLSS'19](https://mlssafrica.com/programme-schedule/) | [YouTube-Lectures](https://www.youtube.com/channel/UC722CmQVgcLtxt_jXr3RyWg/videos) | 2019 |
| 37. | **Deep Learning Boot Camp** | Lots of Legends, Simons Institute, Berkeley | [DLBC-19](https://simons.berkeley.edu/workshops/schedule/10624) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLgKuh-lKre12c2Il9mNX0Cmp9Z4oFNrQh) | 2019 |
| 38. | **Frontiers of Deep Learning** | Lots of Legends, Simons Institute, Berkeley | [FoDL-19](https://simons.berkeley.edu/workshops/schedule/10627) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLgKuh-lKre11ekU7g-Z_qsvjDD8cT-hi9) | 2019 |
| 39. | **Mathematics of data: Structured representations for sensing, approximation and learning** | Lots of Legends, The Alan Turing Institute, London | [MoD-19](https://www.turing.ac.uk/sites/default/files/2019-05/agenda_9_3.pdf) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLuD_SqLtxSdX_w1Ztexpzl_EJgFQSkWez) | 2019 |
| 40. | **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 |
| | | | | | |
| 41. | **The Mathematics of Deep Learning and Data Science** | Lots of Legends, Isaac Newton Institute, Cambridge | [MoDL-DS](https://gateway.newton.ac.uk/event/ofbw46) | [Lecture-Videos](https://gateway.newton.ac.uk/event/ofbw46/programme) | 2019 |
| 42. | **Geometry of Deep Learning** | Lots of Legends, MSR Redmond | [GoDL](https://www.microsoft.com/en-us/research/event/ai-institute-2019) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLD7HFcN7LXRe30qq36It2XCljxc340O_d) | 2019 |
| 43. | **Deep Learning for Science School** | Many folks, LBNL, Berkeley | [DLfSS](https://dl4sci-school.lbl.gov/agenda) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL20S5EeApOSvfvEyhCPOUzU7zkBcR5-eL) | 2019 |
| 44. | **Emerging Challenges in Deep Learning** | Lots of Legends, Simons Institute, Berkeley | [ECDL](https://simons.berkeley.edu/workshops/schedule/10629) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLgKuh-lKre10BpafDrv0fg2VNUweWXWVd) | 2019 |
| 45. | **Full Stack Deep Learning** | Pieter Abbeel and many others, UC Berkeley | [FSDL-M19](https://fullstackdeeplearning.com/march2019) | [YouTube-Lectures-Day-1](https://www.youtube.com/playlist?list=PL1T8fO7ArWlcf3Hc4VMEVBlH8HZm_NbeB)
[Day-2](https://www.youtube.com/playlist?list=PL1T8fO7ArWlf6TWwdstb-PcwlubnlrKrm) | 2019 |
| 46. | **Algorithmic and Theoretical aspects of Machine Learning** | Lots of legends, IIIT-Bengaluru | [ACM-ML](https://india.acm.org/education/machine-learning)
[nptel](https://nptel.ac.in/courses/128/106/128106011/) | [YouTube-Lectures](https://nptel.ac.in/courses/128/106/128106011) | 2019 |
| 47. | **Deep Learning and Reinforcement Learning Summer School** | Lots of Legends, AMII, Edmonton, Canada | [DLRL-2019](https://dlrlsummerschool.ca/past-years) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLKlhhkvvU8-aXmPQZNYG_e-2nTd0tJE8v) | 2019 |
| 48. | **Mathematics of Machine Learning** - Summer Graduate School | Lots of Legends, University of Washington | [MoML-SGS](http://www.msri.org/summer_schools/866#schedule), [MoML-SS](http://mathofml.cs.washington.edu/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLTPQEx-31JXhguCush5J7OGnEORofoCW9) | 2019 |
| 49. | **Workshop on Theory of Deep Learning: Where next?** | Lots of Legends, IAS, Princeton University | [WTDL](https://www.math.ias.edu/wtdl) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLdDZb3TwJPZ5dqqg_S-rgJqSFeH4DQqFQ) | 2019 |
| 50. | **Computational Vision Summer School** | Lots of Legends, Black Forest, Germany | [CVSS-2019](http://orga.cvss.cc/program-cvss-2019/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLeCNfJWZKqxsvidOlVLtWq9s7sIsX1QTC) | 2019 |
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| 51. | **Learning under complex structure** | Lots of Legends, MIT | [LUCS](https://mifods.mit.edu/complex.php) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLM4Pv4KYYzGwhIHcaY6zYR7M9hhFO4Vud) | 2020 |
| 52. | **Machine Learning Summer School** | Lots of Legends, MPI-IS Tübingen (virtual) | [MLSS](http://mlss.tuebingen.mpg.de/2020/schedule.html) | [YouTube-Lectures](https://www.youtube.com/channel/UCBOgpkDhQuYeVVjuzS5Wtxw/videos) | SS2020 |
| 53. | **Eastern European Machine Learning Summer School** | Lots of Legends, Kraków, Poland (virtual) | [EEML](https://www.eeml.eu/program) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLaKY4p4V3gE1j01FOY2FeglV4jRntQj84) | S2020 |
| 54. | **Lisbon Machine Learning Summer School** | Lots of Legends, Lisbon, Portugal (virtual) | [LxMLS](http://lxmls.it.pt/2020/?page_id=19) | [YouTube-Lectures](https://www.youtube.com/channel/UCkVFZWgT1jR75UvSLGP9_mw) | S2020 |
| 55. | **Workshop on New Directions in Optimization, Statistics and Machine Learning** | Lots of Legends, Institute of Advanced Study, Princeton | [ML-Opt new dir.](https://www.ias.edu/video/workshop/2020/0415-16) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLdDZb3TwJPZ4Ri6i0MIdesIEpYK4lx17Q) | 2020 |
| 56. | **Mediterranean Machine Learning School** | Lots of Legends, Italy (virtual) | [M2L-school](https://www.m2lschool.org/talks) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLF-wkqRv4u1YRbfnwN8cXXyrmXld-sked) | 2021 |
| 57. | **Mathematics of Machine Learning - One World Seminar** | Lots of Legends, Virtual | [1W-ML](https://sites.google.com/view/oneworldml/past-events) | [YouTube-Lectures](https://www.youtube.com/channel/UCz7WlgXs20CzugkfxhFCNFg/videos) | 2020 - now |
| 58. | **Deep Learning Theory Summer School** | Lots of Legends, Princeton University (virtual) | [DLT'21](https://deep-learning-summer-school.princeton.edu) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL2mB9GGlueJj_FNjJ8RWgz4Nc_hCSXfMU) | 2021 |
| | | | | | |
[Go to Contents :arrow_heading_up:](https://github.com/kmario23/deep-learning-drizzle#contents)
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### :bird: Bird's Eye view of A(G)I :eagle:
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| S.No | Course Name | University/Instructor(s) | Course WebPage | Lecture Videos | Year |
| ---- | -------------------------------------- | -------------------------------------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | --------- |
| 1. | **Artificial General Intelligence** | Lots of Legends, MIT | [6.S099-AGI](https://agi.mit.edu/) | [Lecture-Videos](https://agi.mit.edu/) | 2018-2019 |
| 2. | **AI Podcast** | Lots of Legends, MIT | [AI-Pod](https://lexfridman.com/ai/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLrAXtmErZgOdP_8GztsuKi9nrraNbKKp4) | 2018-2019 |
| 3. | **NYU - AI Seminars** | Lots of Legends, NYU | [modern-AI](https://engineering.nyu.edu/academics/departments/electrical-and-computer-engineering/ece-seminar-series/modern-artificial) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLhwo5ntex8iY9xhpSwWas451NgVuqBE7U) | 2017-now |
| 4. | **Deep Learning: Alchemy or Science?** | Lots of Legends, Institute for Advanced Study, Princeton | [DLAS](https://video.ias.edu/deeplearning/2019/0222)
[Agenda](https://www.math.ias.edu/tml/dlasagenda) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLdDZb3TwJPZ7aAxhIHALBoh8l6-UxmMNP) | 2019 |
| | | | | | |
[Go to Contents :arrow_heading_up:](https://github.com/kmario23/deep-learning-drizzle#contents)
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### To-Do :running:
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:white_large_square: Optimization courses which form the foundation for ML, DL, RL
:white_large_square: Computer Vision courses which are DL & ML heavy
:white_large_square: Speech recognition courses which are DL heavy
:white_large_square: Structured Courses on Geometric, Graph Neural Networks
:white_large_square: Section on Autonomous Vehicles
:white_large_square: Section on Computer Graphics with ML/DL focus
:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:
[Go to Contents :arrow_heading_up:](https://github.com/kmario23/deep-learning-drizzle#contents)
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### Around the Web :earth_asia:
- [Montreal.AI](http://www.montreal.ai/ai4all.pdf)
- [UPC-DLAI-2018](https://telecombcn-dl.github.io/2018-dlai/)
- [UPC-DLAI-2019](https://telecombcn-dl.github.io/dlai-2019/)
- [www.hashtagtechgeek.com](https://www.hashtagtechgeek.com/2019/10/250-machine-learning-deep-learning-videos-courseware.html)
- [UPC-Barcelona, IDL-2020](https://telecombcn-dl.github.io/idl-2020/)
- [UPC-DLAI-2020](https://telecombcn-dl.github.io/dlai-2020)
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### Contributions :pray:
If 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.
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### Support :moneybag:
**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:
[](https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&hosted_button_id=NT3EATS5N35WU)
Vielen lieben Dank! :blue_heart:
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### :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:
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