📚 "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 |
| Bayesian Deep Learning |
Medical Imaging |
| 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 (Academic Torrent) |
2016 |
| 10. | Neural Networks | Hugo Larochelle, Université de Sherbrooke | Neural-Networks | YouTube-Lectures (Academic Torrent) |
2016 |
| 11. | CS224d: Deep Learning for NLP | Richard Socher, Stanford University | CS224d | YouTube-Lectures (Academic 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 (Academic Torrent) |
2017 |
| 14. | Topics in Deep Learning | Ruslan Salakhutdinov, CMU | 10707 | YouTube-Lectures | F2017 |
| 15. | Deep Learning Crash Course | Leo Isikdogan, UT Austin | None |
YouTube-Lectures | 2017 |
| 16. | Deep Learning | Andrew Ng, Stanford University | CS230 | YouTube-Lectures | 2018 |
| 17. | UvA Deep Learning | Efstratios Gavves, University of Amsterdam | UvA-DLC | Lecture-Videos | 2018 |
| 18. | Advanced Deep Learning and Reinforcement Learning | Many legends, DeepMind | None |
YouTube-Lectures | 2018 |
| 19. | Machine Learning | Peter Bloem, Vrije Universiteit Amsterdam | MLVU | YouTube-Lectures | 2018 |
| 20. | Deep Learning | Francois Fleuret, EPFL | EE-59 | Video-Lectures | 2018 |
| 21. | Introduction to Deep Learning | Alexander Amini, Harini Suresh and others, MIT | 6.S191 | YouTube-Lectures 2017-version |
2017- 2019 |
| 22. | Deep Learning for Self-Driving Cars | Lex Fridman, MIT | 6.S094 | YouTube-Lectures | 2017-2018 |
| 23. | Introduction to Deep Learning | Bhiksha Raj and many others, CMU | 11-485/785 | YouTube-Lectures | S2018 |
| 24. | Introduction to Deep Learning | Bhiksha Raj and many others, CMU | 11-485/785 | YouTube-Lectures Recitation-Inclusive | F2018 |
| 25. | Deep Learning Specialization | Andrew Ng, Stanford | DL.AI | YouTube-Lectures | 2017-2018 |
| 26. | Deep Learning | Ali Ghodsi, University of Waterloo | STAT-946 | YouTube-Lectures | F2017 |
| 27. | Deep Learning | Mitesh Khapra, IIT-Madras | CS7015 | YouTube-Lectures | 2018 |
| 28. | Deep Learning for AI | UPC Barcelona | DLAI-2017 DLAI-2018 |
YouTube-Lectures | 2017-2018 |
| 29. | Deep Learning | Alex Bronstein and Avi Mendelson, Technion | CS236605 | YouTube-Lectures | 2018 |
| 30. | MIT Deep Learning | Many Researchers, Lex Fridman, MIT | 6.S094, 6.S091, 6.S093 | YouTube-Lectures | 2019 |
| 31. | Deep Learning Book companion videos | Ian Goodfellow and others | DL-book slides | YouTube-Lectures | 2017 |
| 33. | Theories of Deep Learning | Many Legends, Stanford | Stats-385 | YouTube-Lectures (first 10 lectures) |
F2017 |
| 34. | Neural Networks | Grant Sanderson | None |
YouTube-Lectures | 2017-2018 |
| 35. | CS230: Deep Learning | Andrew Ng, Kian Katanforoosh, Stanford | CS230 | YouTube-Lectures | A2018 |
| 36. | Theory of Deep Learning | Lots of Legends, Canary Islands | DALI'18 | YouTube-Lectures | 2018 |
| 37. | Introduction to Deep Learning | Alex Smola, UC Berkeley | Stat-157 | YouTube-Lectures | S2019 |
| 38. | Deep Unsupervised Learning | Pieter Abbeel, UC Berkeley | CS294-158 | YouTube-Lectures | S2019 |
| 39. | Machine Learning | Peter Bloem, Vrije Universiteit Amsterdam | MLVU | YouTube-Lectures | 2019 |
| 40. | Deep Learning on Computational Accelerators | Alex Bronstein and Avi Mendelson, Technion | CS236605 | YouTube-Lectures | S2019 |
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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 |
| 12. | Matrix Methods in Data Analysis, Signal Processing, and Machine Learning | Gilbert Strang, MIT | 18.065 | YouTube-Lectures | S2018 |
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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. | Foundations of Optimization | Joydeep Dutta, IIT-Kanpur | None |
YouTube-Lectures | 2014 |
| 7. | Algorithmic Aspects of Machine Learning | Ankur Moitra, MIT | 18.409-AAML | YouTube-Lectures | S2015 |
| 8. | Advanced Algorithms | Ankur Moitra, MIT | 6.854-AA | YouTube-Lectures | S2016 |
| 9 | Introduction to Optimization | Michael Zibulevsky, Technion | None |
YouTube-Lectures | 2016 |
| 10. | Convex Optimization | Ryan Tibshirani, CMU | cvx-opt | YouTube-Lectures | F2018 |
| 11. | 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, Technische Universität München | Machine Learning | YouTube-Lectures | 2013 |
| 6. | Introduction to Machine Learning | Alex Smola, CMU | 10-701 | YouTube-Lectures | 2013 |
| 7. | Introduction to Machine Learning | Alex Smola and Geoffrey Gordon, CMU | 10-701x | YouTube-Lectures | 2013 |
| 8. | Pattern Recognition | Sukhendu Das, IIT-M and C.A. Murthy, ISI-Calcutta | PR-NPTEL | YouTube-Lectures | 2014 |
| 9. | An Introduction to Statistical Learning with Applications in R | Trevor Hastie and Robert Tibshirani, Stanford | stat-learn R-bloggers |
YouTube-Lectures | 2014 |
| 10. | Introduction to Machine Learning | Katie Malone, Sebastian Thrun, Udacity | ML-Udacity | YouTube-Lectures | 2015 |
| 11. | Introduction to Machine Learning | Dhruv Batra, Virginia Tech | ECE-5984 | YouTube-Lectures | 2015 |
| 12. | Statistical Learning - Classification | Ali Ghodsi, University of Waterloo | STAT-441 | YouTube-Lectures | 2015 |
| 13. | Machine Learning Theory | Shai Ben-David, University of Waterloo | None |
YouTube-Lectures | 2015 |
| 14. | Introduction to Machine Learning | Alex Smola, CMU | 10-701 | YouTube-Lectures | S2015 |
| 15. | ML: Supervised Learning | Michael Littman, Charles Isbell, Pushkar Kolhe, GaTech | ML-Udacity | YouTube-Lectures | 2015 |
| 16. | ML: Unsupervised Learning | Michael Littman, Charles Isbell, Pushkar Kolhe, GaTech | ML-Udacity | YouTube-Lectures | 2015 |
| 17. | Machine Learning | Pedro Domingos, UWashington | CSEP-546 | YouTube-Lectures | S2016 |
| 18. | Statistical Machine Learning | Larry Wasserman, CMU | None |
YouTube-Lectures | S2016 |
| 19. | Machine Learning with Large Datasets | William Cohen, CMU | 10-605 | YouTube-Lectures | F2016 |
| 20. | Statistical Learning - Classification | Ali Ghodsi, University of Waterloo | None |
YouTube-Lectures | 2017 |
| 21. | Machine Learning | Andrew Ng, Stanford University | Coursera-ML | YouTube-Lectures | 2017 |
| 22. | Machine Learning | Roni Rosenfield, CMU | 10-601 | YouTube-Lectures | 2017 |
| 23. | Statistical Machine Learning | Ryan Tibshirani, Larry Wasserman, CMU | 10-702 | YouTube-Lectures | S2017 |
| 24. | Machine Learning for Computer Vision | Fred Hamprecht, Heidelberg University | None |
YouTube-Lectures | F2017 |
| 25. | Data Visualization | Ali Ghodsi, University of Waterloo | None |
YouTube-Lectures | 2017 |
| 26. | Machine Learning for Intelligent Systems | Kilian Weinberger, Cornell University | CS4780 | YouTube-Lectures | F2018 |
| 27. | Statistical Learning Theory and Applications | Tomaso Poggio, Lorenzo Rosasco, Sasha Rakhlin | 9.520/6.860 | YouTube-Lectures | F2018 |
| 28. | Machine Learning and Data Mining | Mike Gelbart, University of British Columbia | CPSC-340 | YouTube-Lectures | 2018 |
| 29. | Foundations of Machine Learning | David Rosenberg, Bloomberg | FOML | YouTube-Lectures | 2018 |
| 30. | Introduction to Machine Learning | Andreas Krause, ETH Zuerich | IntroML | YouTube-Lectures | 2018 |
| 31. | Advanced Machine Learning | Joachim Buhmann, ETH Zuerich | AML-18 | YouTube-Lectures | 2018 |
| 32. | Machine Learning Fundamentals | Sanjoy Dasgupta, UC-San Diego | MLF-slides | YouTube-Lectures | 2018 |
| 33. | Machine Learning | Jordan Boyd-Graber, University of Maryland | CMSC-726 | YouTube-Lectures | 2015-2018 |
| 34. | Introduction to Machine Learning | Pascal Poupart, University of Waterloo | CS480/680 | YouTube-Lectures | S2019 |
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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 |
| 12. | Reinforcement Learning and Optimal Control | Dimitri Bertsekas, Arizona State University | RLOC | Lecture-Videos | 2019 |
| 13. | Reinforcement Learning | Emma Brunskill, Stanford University | CS234 | YouTube-Lectures | 2019 |
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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. | Bayesian Neural Networks, Variational Inference | Lots of Legends | None |
YouTube-Lectures | 2014-now |
| 2. | Variational Inference | Chieh Wu, Northeastern University | None |
YouTube-Lectures | 2015 |
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| S.No | Course Name | University/Instructor(s) | Course WebPage | Lecture Videos | Year |
|---|---|---|---|---|---|
| 1. | Medical Imaging Summer School | Lots of Legends, Sicily | MISS-14 | YouTube-Lectures | 2014 |
| 2. | Biomedical Image Analysis Summer School | Lots of Legends, Paris | None |
YouTube-Lectures | 2015 |
| 3. | Medical Imaging Summer School | Lots of Legends, Sicily | MISS-16 | YouTube-Lectures | 2016 |
| 4. | OPtical and UltraSound imaging - OPUS | Lots of Legends, Université de Lyon, France | OPUS'16 | YouTube-Lectures | 2016 |
| 5. | Medical Imaging Summer School | Lots of Legends, Sicily | MISS-18 | YouTube-Lectures | 2018 |
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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 | 2017 |
| 2. | Geometric Deep Learning on Graphs and Manifolds | Michael Bronstein, Technische Universität München | None |
Lec-part1, Lec-part2 |
2017 |
| 3. | Eurographics Symposium on Geometry Processing - Graduate School | Lots of Legends, SIGGRAPH, London | SGP-2017 | YouTube-Lectures | 2017 |
| 4. | Eurographics Symposium on Geometry Processing - Graduate School | Lots of Legends, SIGGRAPH, Paris | SGP-2018 | YouTube-Lectures | 2018 |
| 5. | Geometry and Learning from Data in 3D and Beyond -Geometry and Learning from Data Tutorials | Lots of Legends, IPAM UCLA | GLDT | Lecture-Videos | 2019 |
| 6. | Geometry and Learning from Data in 3D and Beyond - Geometric Processing | Lots of Legends, IPAM UCLA | GeoPro | Lecture-Videos | 2019 |
| 7. | Geometry and Learning from Data in 3D and Beyond - Shape Analysis | Lots of Legends, IPAM UCLA | Shape-Analysis | Lecture-Videos | 2019 |
| 8. | Geometry and Learning from Data in 3D and Beyond - Geometry of Big Data | Lots of Legends, IPAM UCLA | Geo-BData | Lecture-Videos | 2019 |
| 9. | Geometry and Learning from Data in 3D and Beyond - Deep Geometric Learning of Big Data and Applications | Lots of Legends, IPAM UCLA | DGL-BData | Lecture-Videos | 2019 |
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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 |
| 9. | Natural Language Processing with Deep Learning | Abigail See, Chris Manning, Richard Socher, Stanford University | CS224n | 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. | Microsoft Computer Vision Summer School - (classical) | Lots of Legends, Lomonosov Moscow State University | None |
YouTube-Videos Russian-mirror |
2011 |
| 2. | Computer Vision - (classical) | Mubarak Shah, UCF | CAP-5415 | YouTube-Lectures | 2012 |
| 3. | Image and Video Processing: From Mars to Hollywood with a Stop at the Hospital | Guillermo Sapiro, Duke university | None |
YouTube-Videos | 2013 |
| 4. | Computer Vision - (classical) | Mubarak Shah, UCF | CAP-5415 | YouTube-Lectures | 2014 |
| 5. | Multiple View Geometry (classical) | Daniel Cremers, Technische Universität München | mvg | YouTube-Lectures | 2013 |
| 6. | Mathematical Methods for Robotics, Vision, and Graphics | Justin Solomon, Stanford University | CS-205A | YouTube-Lectures | 2013 |
| 7. | Computer Vision for Visual Effects (classical) | Rich Radke, Rensselaer Polytechnic Institute | ECSE-6969 | YouTube-Lectures | S2014 |
| 8. | Autonomous Navigation for Flying Robots | Juergen Sturm, Technische Universität München | Autonavx | YouTube-Lectures | 2014 |
| 9. | SLAM - Mobile Robotics | Cyrill Stachniss, Universitaet Freiburg | RobotMapping | YouTube-Lectures | 2014 |
| 10. | Computational Photography | Irfan Essa, David Joyner, Arpan Chakraborty | CP-Udacity | YouTube-Lectures | 2015 |
| 11. | Lectures on Digital Photography | Marc Levoy, Stanford/Google Research | LoDP | YouTube-Lectures | 2016 |
| 12. | Introduction to Computer Vision (foundation) | Aaron Bobick, Irfan Essa, Arpan Chakraborty | CV-Udacity | YouTube-Lectures | 2016 |
| 13. | Computer Vision | Syed Afaq Ali Shah, University of Western Australia | None |
YouTube-Lectures | 2016 |
| 14. | Deep Learning for Computer Vision | UPC Barcelona | DLCV-16 DLCV-17 DLCV-18 |
YouTube-Lectures | 2016-2018 |
| 15. | Convolutional Neural Networks | Andrew Ng, Stanford University | DeepLearning.AI | YouTube-Lectures | 2017 |
| 16. | Variational Methods for Computer Vision | Daniel Cremers, Technische Universität München | VMCV | YouTube-Lectures | 2017 |
| 17. | Winter School on Computer Vision | Lots of Legends, Israel Institute for Advanced Studies | WS-CV | YouTube-Lectures | 2017 |
| 18. | Deep Learning for Visual Computing | Debdoot Sheet, IIT-Kgp | Nptel Notebooks | YouTube-Lectures | 2018 |
| 19. | The Ancient Secrets of Computer Vision | Joseph Redmon, Ali Farhadi | TASCV ; TASCV-UW | YouTube-Lectures | 2018 |
| 20. | Modern Robotics | Kevin Lynch, Northwestern Robotics | modern-robot | YouTube-Lectures | 2018 |
| 21. | Digial Image Processing | Alex Bronstein, Technion | CS236860 | YouTube-Lectures | 2018 |
| 22. | Mathematics of Imaging - Variational Methods and Optimization in Imaging | Lots of Legends, Institut Henri Poincaré | Workshop-1 | YouTube-Lectures | 2019 |
| 23. | Deep Learning for Video | Xavier Giró, UPC Barcelona | deepvideo | YouTube-Lectures | 2019 |
| 24. | Statistical modeling for shapes and imaging | Lots of Legends, Institut Henri Poincaré, Paris | workshop-2 | YouTube-Lectures | 2019 |
| 25. | Imaging and machine learning | Lots of Legends, Institut Henri Poincaré, Paris | workshop-3 | YouTube-Lectures | 2019 |
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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. | Machine Learning Summer School | Lots of Legends, Pittsburgh | MLSS-14 | YouTube-Lectures | 2014 |
| 6. | Deep Learning Summer School | Lots of Legends, Université de Montréal | DLSS-15 | YouTube-Lectures | 2015 |
| 7. | Biomedical Image Analysis Summer School | Lots of Legends, CentraleSupelec, Paris | None |
YouTube-Lectures | 2015 |
| 8. | Mathematics of Signal Processing | Lots of Legends, Hausdorff Institute for Mathematics | SigProc | YouTube-Lectures | 2016 |
| 9. | Microsoft Research - Machine Learning Course | S V N Vishwanathan and Prateek Jain MS-Research | None |
YouTube-Lectures | 2016 |
| 10. | Deep Learning Summer School | Lots of Legends, Université de Montréal | DL-SS-16 | YouTube-Lectures | 2016 |
| 11. | Lisbon Machine Learning School | Lots of Legends, Instituto Superior Técnico, Portugal | LxMLS-16 | YouTube-Lectures | 2016 |
| 12. | Machine Learning Advances and Applications Seminar | Lots of Legends, Fields Institute, University of Toronto | MLAAS-16 | YouTube-Lectures Video-Lectures |
2016-2017 |
| 13. | Machine Learning Advances and Applications Seminar | Lots of Legends, Fields Institute, University of Toronto | MLAAS-17 | Video Lectures | 2017-2018 |
| 14. | Machine Learning Summer School | Lots of Legends, MPI-IS Tübingen | MLSS-17 | YouTube-Lectures | 2017 |
| 15. | Representation Learning | Lots of Legends, Simons Institute | RepLearn | YouTube-Lectures | 2017 |
| 16. | Foundations of Machine Learning | Lots of Legends, Simons Institute | ML-BootCamp | YouTube-Lectures | 2017 |
| 17. | Optimization, Statistics, and Uncertainty | Lots of Legends, Simons Institute | Optim-Stats | YouTube-Lectures | 2017 |
| 18. | Deep Learning: Theory, Algorithms, and Applications | Lots of Legends, TU-Berlin | DL: TAA | YouTube-Lectures | 2017 |
| 19. | Deep Learning and Reinforcement Learning Summer School | Lots of Legends, Université de Montréal | DLRL-2017 | Lecture-videos | 2017 |
| 20. | Statistical Physics Methods in Machine Learning | Lots of Legends, International Centre for Theoretical Sciences, TIFR | SPMML | YouTube-Lectures | 2017 |
| 21. | Lisbon Machine Learning School | Lots of Legends, Instituto Superior Técnico, Portugal | LxMLS-17 | YouTube-Lectures | 2017 |
| 22. | Interactive Learning | Lots of Legends, Simons Institute, Berkeley | IL-2017 | YouTube-Lectures | 2017 |
| 23. | Computational Challenges in Machine Learning | Lots of Legends, Simons Institute, Berkeley | CCML-17 | YouTube-Lectures | 2017 |
| 24. | Foundations of Data Science | Lots of Legends, Simons Institute | DS-BootCamp | YouTube-Lectures | 2018 |
| 25. | Deep Learning and Bayesian Methods | Lots of Legends, HSE Moscow | DLBM-SS | YouTube-Lectures | 2018 |
| 26. | New Deep Learning Techniques | Lots of Legends, IPAM UCLA | IPAM-Workshop | YouTube-Lectures | 2018 |
| 27. | Deep Learning and Reinforcement Learning Summer School | Lots of Legends, University of Toronto | DLRL-2018 | Lecture-videos | 2018 |
| 28. | Machine Learning Summer School | Lots of Legends, Universidad Autónoma de Madrid, Spain | MLSS-18 | YouTube-Lectures Course-videos |
2018 |
| 29. | Theoretical Basis of Machine Learning | Lots of Legends, International Centre for Theoretical Sciences, TIFR | TBML-18 | Lecture-Videos YouTube-Videos |
2018 |
| 30. | Polish View on Machine Learning | Lots of Legends, Warsaw | PLinML-18 | YouTube-Videos | 2018 |
| 31. | Machine Learning Advances and Applications Seminar | Lots of Legends, Fields Institute, University of Toronto | MLASS | Video Lectures | 2018-2019 |
| 32. | MIFODS- ML, Stats, ToC seminar | Lots of Legends, MIT | MIFODS-seminar | Lecture-videos | 2018-2019 |
| 33. | Learning Machines Seminar Series | Lots of Legends, Cornell Tech | LMSS | YouTube-Lectures | 2018-now |
| 34. | Machine Learning Summer School | Lots of Legends, South Africa | MLSS'19 | YouTube-Lectures | 2019 |
| 35. | Deep Learning Boot Camp | Lots of Legends, Simons Institute, Berkeley | DLBC-19 | YouTube-Lectures | 2019 |
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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 |
| 4. | Deep Learning: Alchemy or Science? | Lots of Legends, Institute for Advanced Study, Princeton | DLAS Agenda |
YouTube-Lectures | 2019 |
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⬜ Optimization courses which form the foundation for ML, DL, RL
⬜ Computer Vision courses which are DL & ML heavy
⬜ Speech recognition courses which are DL heavy
⬜ Structured Courses on Geometric, Graph Neural Networks
⬜ Section on Autonomous Vehicles
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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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