📚 "Read enough so you start developing intuitions and then trust your intuitions and go for it!" 📚
Prof. Geoffrey Hinton, University of Toronto
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| Deep Learning (Deep Neural Networks) |
Probabilistic Graphical Models |
| Machine Learning Fundamentals |
Natural Language Processing |
| Optimization for Machine Learning |
Automatic Speech Recognition |
| General Machine Learning |
Modern Computer Vision |
| Reinforcement Learning |
Boot Camps or Summer Schools |
| Graph Neural Networks | Bird's-eye view of Artificial Intelligence |
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| 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 CSC321-tijmen |
YouTube-Lectures UofT-mirror |
2012 2014 |
| 2. | Neural Networks Demystified | Stephen Welch, Welch Labs | Suppl. Code | YouTube-Lectures | 2014 |
| 3. | Deep Learning at Oxford | Nando de Freitas, Oxford University | Oxford-ML | YouTube-Lectures | 2015 |
| 4. | Deep Learning for Perception | Dhruv Batra, Virginia Tech | ECE-6504 | YouTube-Lectures | 2015 |
| 5. | Deep Learning | Ali Ghodsi, University of Waterloo | STAT-946 | YouTube-Lectures | F2015 |
| 6. | CS231n: CNNs for Visual Recognition | Andrej Karpathy, Stanford University | CS231n | None |
2015 |
| 7. | CS224d: Deep Learning for NLP | Richard Socher, Stanford University | CS224d | YouTube-Lectures | 2015 |
| 8. | Bay Area Deep Learning | Many legends, Stanford | None |
YouTube-Lectures | 2016 |
| 9. | CS231n: CNNs for Visual Recognition | Andrej Karpathy, Stanford University | CS231n | YouTube-Lectures Torrent | 2016 |
| 10. | Neural Networks | Hugo Larochelle, Université de Sherbrooke | Neural-Networks | YouTube-Lectures Torrent | 2016 |
| 11. | CS224d: Deep Learning for NLP | Richard Socher, Stanford University | CS224d | YouTube-Lectures Torrent | 2016 |
| 12. | CS224n: NLP with Deep Learning | Richard Socher, Stanford University | CS224n | YouTube-Lectures | 2017 |
| 13. | CS231n: CNNs for Visual Recognition | Justin Johnson, Stanford University | CS231n | YouTube-Lectures Torrent | 2017 |
| 14. | Deep Learning Crash Course | Leo Isikdogan, UT Austin | None |
YouTube-Lectures | 2017 |
| 15. | Deep Learning | Andrew Ng, Stanford University | CS230 | None |
2018 |
| 16. | UvA Deep Learning | Efstratios Gavves, University of Amsterdam | UvA-DLC | Lecture-Videos | 2018 |
| 17. | Advanced Deep Learning and Reinforcement Learning | Many legends, DeepMind | None |
YouTube-Lectures | 2018 |
| 18. | Deep Learning | Francois Fleuret, EPFL | EE-59 | None |
2019 |
| 19. | Deep Learning | Francois Fleuret, EPFL | EE-59 | Video-Lectures | 2018 |
| 20. | Introduction to Deep Learning | Alexander Amini, Harini Suresh and others, MIT | 6.S191 | YouTube-Lectures 2017-version |
2017- 2019 |
| 21. | Deep Learning for Self-Driving Cars | Lex Fridman, MIT | 6.S094 | YouTube-Lectures | 2017-2018 |
| 22. | Introduction to Deep Learning | Bhiksha Raj and many others, CMU | 11-485/785 | YouTube-Lectures | S2018 |
| 23. | Introduction to Deep Learning | Bhiksha Raj and many others, CMU | 11-485/785 | YouTube-Lectures Recitation-Inclusive | F2018 |
| 24. | Deep Learning Specialization | Andrew Ng, Stanford | DL.AI | YouTube-Lectures | 2017-2018 |
| 25. | Deep Learning | Ali Ghodsi, University of Waterloo | STAT-946 | YouTube-Lectures | F2017 |
| 26. | Deep Learning | Mitesh Khapra, IIT-Madras | CS7015 | YouTube-Lectures | 2018 |
| 27. | Deep Learning for AI | UPC Barcelona | DLAI-2017 DLAI-2018 |
YouTube-Lectures | 2017-2018 |
| 28. | Deep Learning | Alex Bronstein and Avi Mendelson, Technion | CS236605 | YouTube-Lectures | 2018 |
| 29. | MIT Deep Learning | Many Researchers, Lex Fridman, MIT | 6.S094, 6.S091, 6.S093 | YouTube-Lectures | 2019 |
| 30. | Deep Learning Book companion videos | Ian Goodfellow and others | DL-book slides | YouTube-Lectures | 2017 |
| 31. | Neural Networks | Grant Sanderson | None |
YouTube-Lectures | 2017-2018 |
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| S.No | Course Name | University/Instructor(s) | Course Webpage | Video Lectures | Year |
|---|---|---|---|---|---|
| 1. | Linear Algebra | Gilbert Strang, MIT | 18.06 SC | YouTube-Lectures | 2011 |
| 2. | Probability Primer | Jeffrey Miller, Brown University | mathematical monk |
YouTube-Lectures | 2011 |
| 3. | Information Theory, Pattern Recognition, and Neural Networks | David Mackay, University of Cambridge | ITPRNN | YouTube-Lectures | 2012 |
| 4. | Probability and Statistics | Michel van Biezen | None |
YouTube-Lectures | 2015 |
| 5. | Linear Algebra: An in-depth Introduction | Pavel Grinfeld | None |
Part-1 Part-2 Part-3 Part-4 |
2015- 2017 |
| 6. | Multivariable Calculus | Grant Sanderson, Khan Academy | None |
YouTube-Lectures | 2016 |
| 7. | Essence of Linear Algebra | Grant Sanderson | None |
YouTube-Lectures | 2016 |
| 8. | Essence of Calculus | Grant Sanderson | None |
YouTube-Lectures | 2017-2018 |
| 9. | Mathematics for Machine Learning (Linear Algebra, Calculus) | David Dye, Samuel Cooper, and Freddie Page, IC-London | MML | YouTube-Lectures | 2018 |
| 10. | Multivariable Calculus | S.K. Gupta and Sanjeev Kumar, IIT-Roorkee | MVC | YouTube-Lectures | 2018 |
| 11. | Engineering Probability | Rich Radke, Rensselaer Polytechnic Institute | None |
YouTube-Lectures | 2018 |
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| S.No | Course Name | University/Instructor(s) | Course Webpage | Video Lectures | Year |
|---|---|---|---|---|---|
| 1. | Convex Optimization | Stephen Boyd, Stanford University | ee364a | YouTube-Lectures | 2008 |
| 2. | Introduction to Optimization | Michael Zibulevsky, Technion | CS-236330 | YouTube-Lectures | 2009 |
| 3. | Optimization for Machine Learning | S V N Vishwanathan, Purdue University | None |
YouTube-Lectures | 2011 |
| 4. | Optimization | Geoff Gordon & Ryan Tibshirani, CMU | 10-725 | YouTube-Lectures | 2012 |
| 5. | Convex Optimization | Joydeep Dutta, IIT-Kanpur | cvx-nptel | YouTube-Lectures | 2013 |
| 6. | Algorithmic Aspects of Machine Learning | Ankur Moitra, MIT | 18.409-AAML | YouTube-Lectures | S2015 |
| 7. | Advanced Algorithms | Ankur Moitra, MIT | 6.854-AA | YouTube-Lectures | S2016 |
| 8 | Introduction to Optimization | Michael Zibulevsky, Technion | None |
YouTube-Lectures | 2016 |
| 9. | Convex Optimization | Ryan Tibshirani, CMU | cvx-opt | YouTube-Lectures | F2018 |
| 10. | Modern Algorithmic Optimization | Yurii Nesterov, UCLouvain | None |
YouTube-Lectures | 2018 |
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| S.No | Course Name | University/Instructor(s) | Course Webpage | Video Lectures | Year |
|---|---|---|---|---|---|
| 1. | CS229: Machine Learning | Andrew Ng, Stanford University | CS229-old CS229-new |
YouTube-Lectures | 2007 |
| 2. | Machine Learning | Jeffrey Miller, Brown University | mathematical monk |
YouTube-Lectures | 2011 |
| 3. | Machine Learning and Data Mining | Nando de Freitas, University of British Columbia | CPSC-340 | YouTube-Lectures | 2012 |
| 4. | Learning from Data | Yaser Abu-Mostafa, CalTech | CS156 | YouTube-Lectures | 2012 |
| 5. | Machine Learning | Rudolph Triebel, TUM | Machine Learning | YouTube-Lectures | 2013 |
| 6. | Pattern Recognition | Sukhendu Das, IIT-M and C.A. Murthy, ISI-Calcutta | PR-NPTEL | YouTube-Lectures | 2014 |
| 7. | An Introduction to Statistical Learning with Applications in R | Trevor Hastie and Robert Tibshirani, Stanford | stat-learn R-bloggers |
YouTube-Lectures | 2014 |
| 8. | Introduction to Machine Learning | Katie Malone, Sebastian Thrun, Udacity | ML-Udacity | YouTube-Lectures | 2015 |
| 9. | Introduction to Machine Learning | Dhruv Batra, Virginia Tech | ECE-5984 | YouTube-Lectures | 2015 |
| 10. | Statistical Learning - Classification | Ali Ghodsi, University of Waterloo | STAT-441 | YouTube-Lectures | 2015 |
| 11. | Machine Learning Theory | Shai Ben-David, University of Waterloo | None |
YouTube-Lectures | 2015 |
| 12. | Introduction to Machine Learning | Alex Smola, CMU | 10-701 | YouTube-Lectures | S2015 |
| 13. | ML: Supervised Learning | Michael Littman, Charles Isbell, Pushkar Kolhe, GaTech | ML-Udacity | YouTube-Lectures | 2015 |
| 14. | ML: Unsupervised Learning | Michael Littman, Charles Isbell, Pushkar Kolhe, GaTech | ML-Udacity | YouTube-Lectures | 2015 |
| 15. | Machine Learning | Pedro Domingos, UWashington | CSEP-546 | YouTube-Lectures | S2016 |
| 16. | Statistical Machine Learning | Larry Wasserman, CMU | None |
YouTube-Lectures | S2016 |
| 17. | Machine Learning with Large Datasets | William Cohen, CMU | 10-605 | YouTube-Lectures | F2016 |
| 18. | Statistical Learning - Classification | Ali Ghodsi, University of Waterloo | None |
YouTube-Lectures | 2017 |
| 19. | Machine Learning | Andrew Ng, Stanford University | Coursera-ML | YouTube-Lectures | 2017 |
| 20. | Machine Learning | Roni Rosenfield, CMU | 10-601 | YouTube-Lectures | 2017 |
| 21. | Statistical Machine Learning | Ryan Tibshirani, Larry Wasserman, CMU | 10-702 | YouTube-Lectures | S2017 |
| 22. | Machine Learning for Intelligent Systems | Kilian Weinberger, Cornell University | CS4780 | YouTube-Lectures | F2018 |
| 23. | Statistical Learning Theory and Applications | Tomaso Poggio, Lorenzo Rosasco, Sasha Rakhlin | 9.520/6.860 | YouTube-Lectures | F2018 |
| 24. | Machine Learning and Data Mining | Mike Gelbart, University of British Columbia | CPSC-340 | YouTube-Lectures | 2018 |
| 25. | Foundations of Machine Learning | David Rosenberg, Bloomberg | FOML | YouTube-Lectures | 2018 |
| 26. | Introduction to Machine Learning | Andreas Krause, ETH Zuerich | IntroML | YouTube-Lectures | 2018 |
| 27. | Advanced Machine Learning | Joachim Buhmann, ETH Zuerich | AML-18 | YouTube-Lectures | 2018 |
| 28. | Machine Learning Fundamentals | Sanjoy Dasgupta, UC-San Diego | MLF-slides | YouTube-Lectures | 2018 |
| 29. | Machine Learning | Jordan Boyd-Graber, University of Maryland | CMSC-726 | YouTube-Lectures | 2015-2018 |
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| S.No | Course Name | University/Instructor(s) | Course Webpage | Video Lectures | Year |
|---|---|---|---|---|---|
| 1. | Short Course on Reinforcement Learning | Satinder Singh, UMichigan | None |
YouTube-Lectures | 2011 |
| 2. | Approximate Dynamic Programming | Dimitri P. Bertsekas, MIT | Lecture-Slides | YouTube-Lectures | 2014 |
| 3. | Introduction to Reinforcement Learning | David Silver, DeepMind | UCL-RL | YouTube-Lectures | 2015 |
| 4. | Reinforcement Learning | Charles Isbell, Chris Pryby, GaTech; Michael Littman, Brown | RL-Udacity | YouTube-Lectures | 2015 |
| 5. | Reinforcement Learning | Balaraman Ravindran, IIT Madras | RL-IITM | YouTube-Lectures | 2016 |
| 6. | Deep Reinforcement Learning | Sergey Levine, UC Berkeley | CS-294 | YouTube-Lectures | S2017 |
| 7. | Deep Reinforcement Learning | Sergey Levine, UC Berkeley | CS-294 | YouTube-Lectures | F2017 |
| 8. | Deep RL Bootcamp | Many legends, UC Berkeley | Deep-RL | YouTube-Lectures | 2017 |
| 9. | Deep Reinforcement Learning | Sergey Levine, UC Berkeley | CS-294-112 | YouTube-Lectures | 2018 |
| 10. | Reinforcement Learning | Pascal Poupart, University of Waterloo | CS-885 | YouTube-Lectures | 2018 |
| 11. | Deep Reinforcement Learning and Control | Katerina Fragkiadaki and Tom Mitchell, CMU | 10-703 | YouTube-Lectures | 2018 |
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| S.No | Course Name | University/Instructor(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. | Learning with Structured Data: An Introduction to Probabilistic Graphical Models | Christoph Lampert, IST Austria | None |
YouTube-Lectures | 2016 |
| 5. | Probabilistic Graphical Models | Nicholas Zabaras, University of Notre Dame | PGM | YouTube-Lectures | 2018 |
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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 | YouTube-Lectures | 2013-2018 |
| 2. | Deep Learning for Natural Language Processing | Nils Reimers, TU Darmstadt | DL4NLP | YouTube-Lectures | 2015-2017 |
| 3. | Deep Learning for Natural Language Processing | Many Legends, DeepMind-Oxford | DL-NLP | YouTube-Lectures | 2017 |
| 4. | Deep Learning for Speech & Language | UPC Barcelona | DL-SL | Lecture-Videos | 2017 |
| 5. | Neural Networks for Natural Language Processing | Graham Neubig, CMU | NN4NLP Code | YouTube-Lectures | 2017 |
| 6. | Neural Networks for Natural Language Processing | Graham Neubig, CMU | NN4-NLP | YouTube-Lectures | 2018 |
| 7. | Deep Learning for NLP | Min-Yen Kan, NUS | CS-6101 | YouTube-Lectures | 2018 |
| 8. | Neural Networks for Natural Language Processing | Graham Neubig, CMU | NN4NLP | YouTube-Lectures | 2019 |
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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 | Lecture-Videos YouTube-Videos |
2017 |
| 2. | Speech and Audio in the Northeast | Many Legends, Google NYC | SANE-15 | YouTube-Videos | 2015 |
| 3. | Speech and Audio in the Northeast | Many Legends, Google NYC | SANE-17 | YouTube-Videos | 2017 |
| 4. | Speech and Audio in the Northeast | Many Legends, Google Cambridge | SANE-18 | YouTube-Videos | 2018 |
| -1. | Deep Learning for Speech Recognition | Many Legends, AoE | None |
YouTube-Videos | 2015-2018 |
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| S.No | Course Name | University/Instructor(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. | Multiple View Geometry (classical) | Daniel Cremers, TUM | mvg | YouTube-Lectures | 2013 |
| 4. | Computer Vision for Visual Effects (classical) | Rich Radke, Rensselaer Polytechnic Institute | ECSE-6969 | YouTube-Lectures | S2014 |
| 5. | Autonomous Navigation for Flying Robots | Juergen Sturm, TUM | Autonavx | YouTube-Lectures | 2014 |
| 6. | SLAM - Mobile Robotics | Cyrill Stachniss, Universitaet Freiburg | RobotMapping | YouTube-Lectures | 2014 |
| 7. | Computational Photography | Irfan Essa, David Joyner, Arpan Chakraborty | CP-Udacity | YouTube-Lectures | 2015 |
| 8. | Introduction to Computer Vision (foundation) | Aaron Bobick, Irfan Essa, Arpan Chakraborty | CV-Udacity | YouTube-Lectures | 2016 |
| 9. | Deep Learning for Computer Vision | UPC Barcelona | DLCV-16 DLCV-17 DLCV-18 |
YouTube-Lectures | 2016-2018 |
| 10. | Convolutional Neural Networks | Andrew Ng, Stanford University | DeepLearning.AI | YouTube-Lectures | 2017 |
| 11. | Variational Methods for Computer Vision | Daniel Cremers, TUM | VMCV | YouTube-Lectures | 2017 |
| 12. | Winter School on Computer Vision | Lots of Legends, Israel Institute for Advanced Studies | WS-CV | YouTube-Lectures | 2017 |
| 13. | Deep Learning for Visual Computing | Debdoot Sheet, IIT-Kgp | Nptel Notebooks | YouTube-Lectures | 2018 |
| 14. | Modern Robotics | Kevin Lynch, Northwestern Robotics | modern-robot | YouTube-Lectures | 2018 |
| 15. | Digial Image Processing | Alex Bronstein, Technion | CS236860 | YouTube-Lectures | 2018 |
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| S.No | Course Name | University/Instructor(s) | Course WebPage | Lecture Videos | Year |
|---|---|---|---|---|---|
| 1. | Deep Learning, Feature Learning | Lots of Legends, IPAM UCLA | GSS-2012 | YouTube-Lectures | 2012 |
| 2. | Big Data Boot Camp | Lots of Legends, Simons Institute | Big Data | YouTube-Lectures | 2013 |
| 3. | Machine Learning Summer School | Lots of Legends, MPI-IS Tübingen | MLSS-13 | YouTube-Lectures | 2013 |
| 4. | Machine Learning Summer School | Lots of Legends, Reykjavik University | MLSS-14 | YouTube-Lectures | 2014 |
| 5. | Deep Learning Summer School | Lots of Legends, Université de Montréal | DLSS-15 | YouTube-Lectures | 2015 |
| 6. | Mathematics of Signal Processing | Lots of Legends, Hausdorff Institute for Mathematics | SigProc | YouTube-Lectures | 2016 |
| 7. | Microsoft Research - Machine Learning Course | S V N Vishwanathan and Prateek Jain MS-Research | None |
YouTube-Lectures | 2016 |
| 8. | Deep Learning Summer School | Lots of Legends, Université de Montréal | DL-SS-16 | YouTube-Lectures | 2016 |
| 9. | Machine Learning Advances and Applications Seminar | Lots of Legends, Fields Institute, University of Toronto | MLAAS-16 | YouTube-Lectures Video-Lectures |
2016-2017 |
| 10. | Machine Learning Advances and Applications Seminar | Lots of Legends, Fields Institute, University of Toronto | MLAAS-17 | Video Lectures | 2017-2018 |
| 11. | Machine Learning Summer School | Lots of Legends, MPI-IS Tübingen | MLSS-17 | YouTube-Lectures | 2017 |
| 12. | Representation Learning | Lots of Legends, Simons Institute | RepLearn | YouTube-Lectures | 2017 |
| 13. | Foundations of Machine Learning | Lots of Legends, Simons Institute | ML-BootCamp | YouTube-Lectures | 2017 |
| 14. | Optimization, Statistics, and Uncertainty | Lots of Legends, Simons Institute | Optim-Stats | YouTube-Lectures | 2017 |
| 15. | Deep Learning: Theory, Algorithms, and Applications | Lots of Legends, TU-Berlin | DL: TAA | YouTube-Lectures | 2017 |
| 16. | Deep Learning and Reinforcement Learning Summer School | Lots of Legends, Université de Montréal | DLRL-2017 | Lecture-videos | 2017 |
| 17. | Statistical Physics Methods in Machine Learning | Lots of Legends, International Centre for Theoretical Sciences, TIFR | SPMML | YouTube-Lectures | 2017 |
| 18. | Foundations of Data Science | Lots of Legends, Simons Institute | DS-BootCamp | YouTube-Lectures | 2018 |
| 19. | Deep Learning and Bayesian Methods | Lots of Legends, HSE Moscow | DLBM-SS | YouTube-Lectures | 2018 |
| 20. | New Deep Learning Techniques | Lots of Legends, IPAM UCLA | IPAM-Workshop | YouTube-Lectures | 2018 |
| 21. | Deep Learning and Reinforcement Learning Summer School | Lots of Legends, University of Toronto | DLRL-2018 | Lecture-videos | 2018 |
| 22. | Machine Learning Advances and Applications Seminar | Lots of Legends, Fields Institute, University of Toronto | MLASS | Video Lectures | 2018-2019 |
| 23. | MIFODS- ML, Stats, ToC seminar | Lots of Legends, MIT | MIFODS-seminar | Lecture-videos | 2018-2019 |
| 24. | Learning Machines Seminar Series | Lots of Legends, Cornell Tech | LMSS | YouTube-Lectures | 2018-now |
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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 | Lecture-Videos | 2018-2019 |
| 2. | AI Podcast | Lots of Legends, MIT | AI-Pod | YouTube-Lectures | 2018-2019 |
| 3. | NYU - AI Seminars | Lots of Legends, NYU | modern-AI | YouTube-Lectures | 2017-now |
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⬜ Optimization courses which form the foundation for ML, DL, RL
⬜ Computer Vision courses which are DL & ML heavy
⬜ NLP courses which are DL, RL, & ML heavy
⬜ Speech recognition courses which are DL heavy
⬜ Structured Courses on Geometric, Graph Neural Networks,
⬜ Section on DL/RL/ML Summer School Lectures
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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.
Danke Sehr!
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