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 Videos-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. | CS229: Machine Learning | Andrew Ng, Stanford University | CS229 | YouTube-Lectures-2014 | 2017 |
11. | Deep Learning | Andrew Ng, Stanford University | CS230 | None |
2018 |
12. | Bay Area Deep Learning | Many legends | None |
YouTube-Lectures | 2016 |
13. | UvA Deep Learning | Efstratios Gavves, University of Amsterdam(UvA) | UvA-DLC | Lecture-Videos | 2018 |
14. | Advanced Deep Learning and Reinforcement Learning | Many legends, DeepMind | None |
YouTube-Lectures | 2018 |
15. | Deep Learning | Francois Fleuret, EPFL | EE-59 | None |
2019 |
16. | Deep Learning | Francois Fleuret, EPFL | EE-59 | Video-Lectures | 2018 |
17. | Deep Learning for Perception | Dhruv Batra, Virginia Tech | ECE-6504 | YouTube-Lectures | 2015 |
18. | Introduction to Deep Learning | Alexander Amini, Harini Suresh, MIT | 6.S191 | YouTube-Lectures | 2018 |
19. | Deep Learning for Self-Driving Cars | Lex Fridman, MIT | 6.S094 | YouTube-Lectures | 2017-2018 |
20. | MIT Deep Learning | Many Researchers, Lex Fridman, MIT |
6.S094, 6.S091, 6.S093 | YouTube-Lectures | 2019 |
21. | Introduction to Deep Learning | Biksha Raj and many others, CMU | 11-485/785 | YouTube-Lectures | 2018 |
22. | Deep Learning Specialization | Andrew Ng, Stanford | DeepLearning.AI | YouTube-Lectures | 2017-2018 |
23. | Deep Learning, Feature Learning | Many legends, IPAM UCLA | GSS-2012 | YouTube-Lectures | 2012 |
24. | New Deep Learning Techniques | Many Legends, IPAM UCLA | IPAM-Workshop | YouTube-Lectures | 2018 |
25. | Deep|Bayes | Many Legends | DeepBayes.ru | YouTube-Lectures | 2018 |
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. | Introduction to Machine Learning | Dhruv Batra, Virginia Tech | ECE-5984 | YouTube-Lectures | 2015 |
3. | Machine Learning | Andrew Ng, Stanford University | Coursera-ML | YouTube-Lectures | 2017 |
Reinforcement Learning
S.No | Course Name | University/Teacher(s) | Course Webpage | Video Lectures | Year |
---|---|---|---|---|---|
1. | Approximate Dynamic Programming | Dimitri P. Bertsekas | 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 | Spring-2017 |
5. | Deep Reinforcement Learning | Sergey Levine, UC Berkeley | CS-294 | YouTube-Lectures | Fall-2017 |
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 |
If you find a course that fits in any of the three categories above (i.e. DL, ML, RL), 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!