| S.No | Course Name | University/Teacher(s) | Course WebPage | Lecture Videos | Year |
|---|---|---|---|---|---|
| 1. | Neural Networks for Machine Learning | Geoffrey Hinton, University of Toronto | Lecture-Slides CSC321-tijmen |
YouTube-Lectures mirror |
2012 2014 |
| 2. | Deep Learning at Oxford | Nando de Freitas, Oxford University | Oxford-ML | YouTube-Lectures | 2015 |
| 3. | CS231n: CNNs for Visual Recognition | Andrej Karpathy, Stanford University | CS231n | None |
2015 |
| 4. | CS231n: CNNs for Visual Recognition | Andrej Karpathy, Stanford University | CS231n | YouTube-Lectures | 2016 |
| 5. | CS231n: CNNs for Visual Recognition | Justin Johnson, Stanford University | CS231n | YouTube-Lectures | 2017 |
| 6. | CS224d: Deep Learning for NLP | Richard Socher, Stanford University | CS224d | YouTube-Lectures | 2015 |
| 7. | CS224d: Deep Learning for NLP | Richard Socher, Stanford University | CS224d | YouTube-Lectures | 2016 |
| 8. | CS224n: NLP with Deep Learning | Richard Socher, Stanford University | CS224n | YouTube-Lectures | 2017 |
| 9. | Neural Networks | Hugo Larochelle, Université de Sherbrooke | Neural-Networks | YouTube-Lectures | 2016 |
| 10. | Deep Learning | Andrew Ng, Stanford University | CS230 | None |
2018 |
| 11. | Bay Area Deep Learning | Many legends | None |
YouTube-Lectures | 2016 |
| 12. | UvA Deep Learning | Efstratios Gavves, University of Amsterdam(UvA) | UvA-DLC | Lecture-Videos | 2018 |
| 13. | Advanced Deep Learning and Reinforcement Learning | Many legends, DeepMind | None |
YouTube-Lectures | 2018 |
| 14. | Deep Learning | Francois Fleuret, EPFL | EE-59 | None |
2019 |
| 15. | Deep Learning | Francois Fleuret, EPFL | EE-59 | Video-Lectures | 2018 |
| 16. | Deep Learning for Perception | Dhruv Batra, Virginia Tech | ECE-6504 | YouTube-Lectures | 2015 |
| 17. | Introduction to Deep Learning | Alexander Amini, Harini Suresh, MIT | 6.S191 | YouTube-Lectures | 2018 |
| 18. | Deep Learning for Self-Driving Cars | Lex Fridman, MIT | 6.S094 | YouTube-Lectures | 2017-2018 |
| 19. | MIT Deep Learning | Many Researchers, Lex Fridman, MIT |
6.S094, 6.S091, 6.S093 | YouTube-Lectures | 2019 |
| 20. | Introduction to Deep Learning | Biksha Raj and many others, CMU | 11-485/785 | YouTube-Lectures | S2018 |
| 21. | Introduction to Deep Learning | Biksha Raj and others, CMU | 11-485/785 | YouTube-Lectures Recitation-Inclusive |
F2018 |
| 22. | Deep Learning Specialization | Andrew Ng, Stanford | DeepLearning.AI | YouTube-Lectures | 2017-2018 |
| 23. | Deep Learning | Ali Ghodsi, University of Waterloo | STAT-946 | YouTube-Lectures | F2015 |
| 24. | Deep Learning | Ali Ghodsi, University of Waterloo | STAT-946 | YouTube-Lectures | F2017 |
| 25. | Deep Learning, Feature Learning | Many legends, IPAM UCLA | GSS-2012 | YouTube-Lectures | 2012 |
| 26. | New Deep Learning Techniques | Many Legends, IPAM UCLA | IPAM-Workshop | YouTube-Lectures | 2018 |
| 27. | Deep|Bayes | Many Legends | DeepBayes.ru | YouTube-Lectures | 2018 |
| -1. | Deep Learning Book companion videos | Ian Goodfellow and others | DL-book slides | YouTube-Lectures | 2017 |
🎢 General Machine Learning 💥
| S.No | Course Name | University/Teacher(s) | Course Webpage | Video Lectures | Year |
|---|---|---|---|---|---|
| 1. | Learning from Data | Yaser Abu-Mostafa, CalTech | CS156 | YouTube-Lectures | 2012 |
| 2. | Machine Learning | Rudolph Triebel, TUM | Machine Learning | YouTube-Lectures | 2013 |
| 3. | Introduction to Machine Learning | Dhruv Batra, Virginia Tech | ECE-5984 | YouTube-Lectures | 2015 |
| 4. | Statistical Learning - Classification | Ali Ghodsi, University of Waterloo | STAT-441 | YouTube-Lectures | 2015 |
| 5. | Introduction to Machine Learning | Alex Smola, CMU | 10-701 | YouTube-Lectures | S2015 |
| 6. | Statistical Machine Learning | Larry Wasserman | None |
YouTube-Lectures | S2016 |
| 7. | Statistical Learning - Classification | Ali Ghodsi, University of Waterloo | None |
YouTube-Lectures | 2017 |
| 8. | Machine Learning | Andrew Ng, Stanford University | Coursera-ML | YouTube-Lectures | 2017 |
| 9. | CS229: Machine Learning | Andrew Ng, Stanford University | CS229 | YouTube-Lectures-2014 | 2017 |
| 10. | Statistical Machine Learning | Ryan Tibshirani, Larry Wasserman, CMU | 10-702 | YouTube-Lectures | S2017 |
| 11. | Machine Learning for Intelligent Systems | Kilian Weinberger, Cornell University | CS4780 | YouTube-Lectures | F2018 |
🎈 Reinforcement Learning ♨️ 🎮
| S.No | Course Name | University/Teacher(s) | Course Webpage | Video Lectures | Year |
|---|---|---|---|---|---|
| 1. | Approximate Dynamic Programming | Dimitri P. Bertsekas, MIT | Lecture-Slides | YouTube-Lectures | 2014 |
| 2. | Introduction to Reinforcement Learning | David Silver, DeepMind | UCL-RL | YouTube-Lectures | 2015 |
| 3. | Reinforcement Learning | Balaraman Ravindran, IIT Madras | RL-IITM | YouTube-Lectures | 2016 |
| 4. | Deep Reinforcement Learning | Sergey Levine, UC Berkeley | CS-294 | YouTube-Lectures | S2017 |
| 5. | Deep Reinforcement Learning | Sergey Levine, UC Berkeley | CS-294 | YouTube-Lectures | F2017 |
| 6. | Deep Reinforcement Learning | Sergey Levine, UC Berkeley | CS-294-112 | YouTube-Lectures | 2018 |
| 7. | Deep RL Bootcamp | Many legends | Deep-RL | YouTube-Lectures | 2017 |
| 8. | Reinforcement Learning | Pascal Poupart, University of Waterloo | CS-885 | YouTube-Lectures | 2018 |
| 9. | Deep Reinforcement Learning and Control | Katerina Fragkiadaki and Tom Mitchell, CMU | 10-703 | YouTube-Lectures | 2018 |
📢 Probabilistic Graphical Models - (Foundation for Graph Neural Networks) ✨
| S.No | Course Name | University/Teacher(s) | Course WebPage | Lecture Videos | Year |
|---|---|---|---|---|---|
| 1. | Probabilistic Graphical Models | Many Legends, MPI-IS | MLSS-Tuebingen | YouTube-Lectures | 2013 |
| 2. | Probabilistic Modeling and Machine Learning | Zoubin Ghahramani, University of Cambridge | WUST-Wroclaw | YouTube-Lectures | 2013 |
| 3. | Probabilistic Graphical Models | Eric Xing, CMU | 10-708 | YouTube-Lectures | 2014 |
| 4. | Probabilistic Graphical Models | Nicholas Zabaras, University of Notre Dame | PGM | YouTube-Lectures | 2018 |
🌺 Natural Language Processing - (More Applied) 🌸
| S.No | Course Name | University/Teacher(s) | Course WebPage | Lecture Videos | Year |
|---|---|---|---|---|---|
| 1. | Deep Learning for Natural Language Processing | Nils Reimers, TU Darmstadt | DL4NLP | YouTube-Lectures | 2015-2017 |
| 2. | Deep Learning for Natural Language Processing | Many Legends, DeepMind-Oxford | DL-NLP | YouTube-Lectures | 2017 |
| 3. | Neural Networks for Natural Language Processing | Graham Neubig, CMU | NN4NLP Code |
YouTube-Lectures | 2017 |
| 4. | Neural Networks for Natural Language Processing | Graham Neubig, CMU | NN4-NLP | YouTube-Lectures | 2018 |
| 5. | Neural Networks for Natural Language Processing | Graham Neubig, CMU | NN4NLP | YouTube-Lectures | 2019 |
🔥 Modern Computer Vision 🎥 📷
| S.No | Course Name | University/Teacher(s) | Course WebPage | Lecture Videos | Year |
|---|---|---|---|---|---|
| 1. | Computer Vision - (classical) | Mubarak Shah, UCF | CAP-5415 | YouTube-Lectures | 2012 |
| 2. | Computer Vision - (classical) | Mubarak Shah, UCF | CAP-5415 | YouTube-Lectures | 2014 |
| 3. | Introduction to Computer Vision (foundation) | Aaron Bobick, Irfan Essa, Arpan Chakraborty | CV-Udacity | YouTube-Lectures | 2016 |
| 4. | Convolutional Neural Networks | Andrew Ng, Stanford | DeepLearning.AI | YouTube-Lectures | 2017 |
| 5. | Variational Methods for Computer Vision | Daniel Cremers, TUM | VMCV | YouTube-Lectures | 2017 |
| 6. | Deep Learning for Visual Computing | Debdoot Sheet, IIT-Kgp | Nptel Notebooks |
YouTube-Lectures | 2018 |
| 7. | Autonomous Navigation for Flying Robots | Juergen Sturm, TUM | Autonavx | YouTube-Lectures | 2014 |
| 8. | SLAM - Mobile Robotics | Cyrill Stachniss, Universitaet Freiburg | RobotMapping | YouTube-Lectures | 2014 |
⬜ Computer Vision courses which are DL & ML heavy
⬜ NLP courses which are DL, RL, & ML heavy
⬜ Speech recognition courses which are DL heavy
⬜ Add courses on Graph Neural Networks
⬜ Add DL/RL Summer School lectures
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 - optional), then please raise an issue or send a PR by updating the course according to the above format.
Thanks!