📚 "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 and its Applications | François Pitié, Trinity College Dublin | EE4C16 | YouTube-Lectures | 2017 |
| 17. | Deep Learning | Andrew Ng, Stanford University | CS230 | YouTube-Lectures | 2018 |
| 18. | UvA Deep Learning | Efstratios Gavves, University of Amsterdam | UvA-DLC | Lecture-Videos | 2018 |
| 19. | Advanced Deep Learning and Reinforcement Learning | Many legends, DeepMind | None |
YouTube-Lectures | 2018 |
| 20. | Machine Learning | Peter Bloem, Vrije Universiteit Amsterdam | MLVU | YouTube-Lectures | 2018 |
| 21. | Deep Learning | Francois Fleuret, EPFL | EE-59 | Video-Lectures | 2018 |
| 22. | Introduction to Deep Learning | Alexander Amini, Harini Suresh and others, MIT | 6.S191 | YouTube-Lectures 2017-version |
2017- 2021 |
| 23. | Deep Learning for Self-Driving Cars | Lex Fridman, MIT | 6.S094 | YouTube-Lectures | 2017-2018 |
| 24. | Introduction to Deep Learning | Bhiksha Raj and many others, CMU | 11-485/785 | YouTube-Lectures | S2018 |
| 25. | Introduction to Deep Learning | Bhiksha Raj and many others, CMU | 11-485/785 | YouTube-Lectures Recitation-Inclusive | F2018 |
| 26. | Deep Learning Specialization | Andrew Ng, Stanford | DL.AI | YouTube-Lectures | 2017-2018 |
| 27. | Deep Learning | Ali Ghodsi, University of Waterloo | STAT-946 | YouTube-Lectures | F2017 |
| 28. | Deep Learning | Mitesh Khapra, IIT-Madras | CS7015 | YouTube-Lectures | 2018 |
| 29. | Deep Learning for AI | UPC Barcelona | DLAI-2017 DLAI-2018 |
YouTube-Lectures | 2017-2018 |
| 30. | Deep Learning | Alex Bronstein and Avi Mendelson, Technion | CS236605 | YouTube-Lectures | 2018 |
| 31. | MIT Deep Learning | Many Researchers, Lex Fridman, MIT | 6.S094, 6.S091, 6.S093 | YouTube-Lectures | 2019 |
| 32. | 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 |
| 41. | Introduction to Deep Learning | Bhiksha Raj and many others, CMU | 11-785 | YouTube-Lectures | S2019 |
| 42. | Introduction to Deep Learning | Bhiksha Raj and many others, CMU | 11-785 | YouTube-Lectures Recitations |
F2019 |
| 43. | UvA Deep Learning | Efstratios Gavves, University of Amsterdam | UvA-DLC | Lecture-Videos | S2019 |
| 44. | Deep Learning | Prabir Kumar Biswas, IIT Kgp | None |
YouTube-Lectures | 2019 |
| 45. | Deep Learning and its Applications | Aditya Nigam, IIT Mandi | CS-671 | YouTube-Lectures | 2019 |
| 46. | Neural Networks | Neil Rhodes, Harvey Mudd College | CS-152 | YouTube-Lectures | F2019 |
| 47. | Deep Learning | Thomas Hofmann, ETH Zürich | DAL-DL | Lecture-Videos | F2019 |
| 48. | Deep Learning | Milan Straka, Charles University | NPFL114 | Lecture-Videos | S2019 |
| 49. | UvA Deep Learning | Efstratios Gavves, University of Amsterdam | UvA-DLC-19 | Lecture-Videos | F2019 |
| 50. | Artificial Intelligence: Principles and Techniques | Percy Liang and Dorsa Sadigh, Stanford University | CS221 | YouTube-Lectures | F2019 |
| 51. | Analyses of Deep Learning | Lots of Legends, Stanford University | STATS-385 | YouTube-Lectures | 2017-2019 |
| 52. | Deep Learning Foundations and Applications | Debdoot Sheet and Sudeshna Sarkar, IIT-Kgp | AI61002 | YouTube-Lectures | S2020 |
| 53. | Designing, Visualizing, and Understanding Deep Neural Networks | John Canny, UC Berkeley | CS 182/282A | YouTube-Lectures | S2020 |
| 54. | Deep Learning | Yann LeCun and Alfredo Canziani, NYU | DS-GA 1008 | YouTube-Lectures | S2020 |
| 55. | Introduction to Deep Learning | Bhiksha Raj, CMU | 11-785 | YouTube-Lectures | S2020 |
| 56. | Deep Unsupervised Learning | Pieter Abbeel, UC Berkeley | CS294-158 | YouTube-Lectures | S2020 |
| 57. | Machine Learning | Peter Bloem, Vrije Universiteit Amsterdam | VUML | YouTube-Lectures | S2020 |
| 58. | Deep Learning (with PyTorch) | Alfredo Canziani and Yann LeCun, NYU | DS-GA 1008 | YouTube-Lectures | S2020 |
| 59. | Introduction to Deep Learning and Generative Models | Sebastian Raschka, UW-Madison | Stat453 | YouTube-Lectures | S2020 |
| 60. | Deep Learning | Andreas Maier, FAU Erlangen-Nürnberg | DL-2020 | YouTube-Lectures Lecture-Videos |
SS2020 |
| 61. | Introduction to Deep Learning | Laura Leal-Taixé and Matthias Niessner, TU-München | I2DL-IN2346 | YouTube-Lectures | SS2020 |
| 62. | Deep Learning | Sargur Srihari, SUNY-Buffalo | CSE676 | YouTube-Lectures-P1 YouTube-Lectures-P2 |
2020 |
| 63. | Deep Learning Lecture Series | Lots of Legends, DeepMind x UCL, London | DLLS-20 | YouTube-Lectures | 2020 |
| 64. | MultiModal Machine Learning | Louis-Philippe Morency & others, Carnegie Mellon University | 11-777 MMML-20 | YouTube-Lectures | F2020 |
| 65. | Reliable and Interpretable Artificial Intelligence | Martin Vechev, ETH Zürich | RIAI-20 | YouTube-Lectures | F2020 |
| 66. | Fundamentals of Deep Learning | David McAllester, Toyota Technological Institute, Chicago | TTIC-31230 | YouTube-Lectures | F2020 |
| 67. | Foundations of Deep Learning | Soheil Feize, University of Maryland, College Park | CMSC 828W | YouTube-Lectures | F2020 |
| 68. | Deep Learning | Andreas Geiger, Universität Tübingen | DL-UT | YouTube-Lectures | W20/21 |
| 69. | Deep Learning | Andreas Maier, FAU Erlangen-Nürnberg | DL-FAU | YouTube-Lectures | W20/21 |
| 70. | Fundamentals of Deep Learning | Terence Parr and Yannet Interian, University of San Francisco | DL-Fundamentals | YouTube-Lectures | S2021 |
| 71. | Full Stack Deep Learning | Pieter Abbeel, Sergey Karayev, UC Berkeley | FS-DL | YouTube-Lectures | S2021 |
| 72. | Deep Learning: Designing, Visualizing, and Understanding DNNs | Sergey Levine, UC Berkeley | CS 182 | YouTube-Lectures | S2021 |
| 73. | Deep Learning in the Life Sciences | Manolis Kellis, MIT | 6.874 | YouTube-Lectures | S2021 |
| 74. | Introduction to Deep Learning and Generative Models | Sebastian Raschka, University of Wisconsin-Madison | Stat 453 | YouTube-Lectures | S2021 |
| 75. | Deep Learning | Alfredo Canziani and Yann LeCun, NYU | NYU-DLSP21 | YouTube-Lectures | S2021 |
| 76. | Applied Deep Learning | Alexander Pacha, TU Wien | None |
YouTube-Lectures | 2020-2021 |
| 77. | Machine Learning | Hung-yi Lee, National Taiwan University | ML'21 | YouTube-Lectures | S2021 |
| 78. | Mathematics of Deep Learning | Lots of legends, FAU | MoDL | Lecture-Videos | 2019-21 |
| 79. | Deep Learning | Peter Bloem, Michael Cochez, and Jakub Tomczak, VU-Amsterdam | DL | YouTube-Lectures | 2020-21 |
| 80. | Applied Deep Learning | Maziar Raissi, UC Boulder | ADL'21 | YouTube-Lectures | 2021 |
| 81. | An Introduction to Group Equivariant Deep Learning | Erik J. Bekkers, Universiteit van Amsterdam | UvAGEDL | YouTube-Lectures | 2022 |
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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. | Linear Algebra Review | Zico Kolter, CMU | LinAlg | YouTube-Lectures | 2013 |
| 5. | Probability and Statistics | Michel van Biezen | None |
YouTube-Lectures | 2015 |
| 6. | Linear Algebra: An in-depth Introduction | Pavel Grinfeld | None |
Part-1 Part-2 Part-3 Part-4 |
2015- 2017 |
| 7. | Multivariable Calculus | Grant Sanderson, Khan Academy | None |
YouTube-Lectures | 2016 |
| 8. | Essence of Linear Algebra | Grant Sanderson | None |
YouTube-Lectures | 2016 |
| 9. | Essence of Calculus | Grant Sanderson | None |
YouTube-Lectures | 2017-2018 |
| 10. | Math Background for Machine Learning | Geoff Gordon, CMU | 10-606, 10-607 | YouTube-Lectures | F2017 |
| 11. | Mathematics for Machine Learning (Linear Algebra, Calculus) | David Dye, Samuel Cooper, and Freddie Page, IC-London | MML | YouTube-Lectures | 2018 |
| 12. | Multivariable Calculus | S.K. Gupta and Sanjeev Kumar, IIT-Roorkee | MVC | YouTube-Lectures | 2018 |
| 13. | Engineering Probability | Rich Radke, Rensselaer Polytechnic Institute | None |
YouTube-Lectures | 2018 |
| 14. | Matrix Methods in Data Analysis, Signal Processing, and Machine Learning | Gilbert Strang, MIT | 18.065 | YouTube-Lectures | S2018 |
| 15. | Information Theory | Himanshu Tyagi, IISC, Bengaluru | E2 201 | YouTube-Lectures | 2018-20 |
| 16. | Math Camp | Mark Walker, University of Arizona | UAMathCamp / Econ-519 | YouTube-Lectures | 2019 |
| 17. | A 2020 Vision of Linear Algebra | Gilbert Strang, MIT | VoLA | YouTube-Lectures | S2020 |
| 18. | Mathematics for Numerical Computing and Machine Learning | Szymon Rusinkiewicz, Princeton University | COS-302 | YouTube-Lectures | F2020 |
| 19. | Essential Statistics for Neuroscientists | Philipp Berens, Universität Klinikum Tübingen | None |
YouTube-Lectures | 2020 |
| 20. | Mathematics for Machine Learning | Ulrike von Luxburg, Eberhard Karls Universität Tübingen | Math4ML | YouTube-Lectures | W2020 |
| 21. | Introduction to Causal Inference | Brady Neal, Mila, Montréal | CausalInf | YouTube-Lectures | F2020 |
| 22. | Applied Linear Algebra | Andrew Thangaraj, IIT Madras | EE5120 | YouTube-Lectures | 2021 |
| 23. | Mathematical Tools for Data Science | Carlos Fernandez-Granda, New York University | DS-GA 1013/Math-GA 2824 | YouTube-Lectures | 2021 |
| 24. | Mathematics for Numerical Computing and Machine Learning | Ryan Adams, Princeton University | COS 302 / SML 305 | YouTube-Lectures | 2021 |
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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 | fop-nptel | YouTube-Lectures | 2014 |
| 7. | Algorithmic Aspects of Machine Learning | Ankur Moitra, MIT | 18.409-AAML | YouTube-Lectures | S2015 |
| 8. | Numerical Optimization | Shirish K. Shevade, IISC | None |
YouTube-Lectures | 2015 |
| 9. | Convex Optimization | Ryan Tibshirani, CMU | 10-725 | YouTube-Lectures | S2015 |
| 10. | Convex Optimization | Ryan Tibshirani, CMU | 10-725 | YouTube-Lectures | F2015 |
| 11. | Advanced Algorithms | Ankur Moitra, MIT | 6.854-AA | YouTube-Lectures | S2016 |
| 12. | Introduction to Optimization | Michael Zibulevsky, Technion | None |
YouTube-Lectures | 2016 |
| 13. | Convex Optimization | Javier Peña & Ryan Tibshirani | 10-725/36-725 | YouTube-Lectures | F2016 |
| 14. | Convex Optimization | Ryan Tibshirani, CMU | 10-725 | YouTube-Lectures Lecture-Videos |
F2018 |
| 15. | Modern Algorithmic Optimization | Yurii Nesterov, UCLouvain | None |
YouTube-Lectures | 2018 |
| 16. | Optimization, Foundations of Optimization | Mark Walker, University of Arizona | MathCamp-20 | YouTube-Lectures-Found. YouTube-Lectures-Opt |
2019 - now |
| 17. | Optimization: Principles and Algorithms | Michel Bierlaire, École polytechnique fédérale de Lausanne (EPFL) | opt-algo | YouTube-Lectures | 2019 |
| 18. | Optimization and Simulation | Michel Bierlaire, École polytechnique fédérale de Lausanne (EPFL) | opt-sim | YouTube-Lectures | S2019 |
| 19. | Brazilian Workshop on Continuous Optimization | Lots of Legends, Instituto Nacional de Matemática Pura e Aplicada, Rio de Janeiro | cont. opt. | YouTube-Lectures | 2019 |
| 20. | One World Optimization Seminar | Lots of Legends, Universität Wien | 1W-OPT | YouTube-Lectures | 2020- |
| 21. | Convex Optimization II | Constantine Caramanis, UT Austin | CVX-Optim-II | YouTube-Lectures | S2020 |
| 22. | Combinatorial Optimization | Constantine Caramanis, UT Austin | comb-op | YouTube-Lectures | F2020 |
| 23. | Optimization Methods for Machine Learning and Engineering | Julius Pfrommer, Jürgen Beyerer, Karlsruher Institut für Technologie (KIT) | Optim-MLE, slides | YouTube-Lectures | W2020-21 |
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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 | Tom Mitchell, CMU | 10-701 | Lecture-Videos | 2011 |
| 4. | Machine Learning and Data Mining | Nando de Freitas, University of British Columbia | CPSC-340 | YouTube-Lectures | 2012 |
| 5. | Learning from Data | Yaser Abu-Mostafa, CalTech | CS156 | YouTube-Lectures | 2012 |
| 6. | Machine Learning | Rudolph Triebel, Technische Universität München | Machine Learning | YouTube-Lectures | 2013 |
| 7. | Introduction to Machine Learning | Alex Smola, CMU | 10-701 | YouTube-Lectures | 2013 |
| 8. | Introduction to Machine Learning | Alex Smola and Geoffrey Gordon, CMU | 10-701x | YouTube-Lectures | 2013 |
| 9. | Pattern Recognition | Sukhendu Das, IIT-M and C.A. Murthy, ISI-Calcutta | PR-NPTEL | YouTube-Lectures | 2014 |
| 10. | An Introduction to Statistical Learning with Applications in R | Trevor Hastie and Robert Tibshirani, Stanford | stat-learn R-bloggers |
YouTube-Lectures | 2014 |
| 11. | Introduction to Machine Learning | Katie Malone, Sebastian Thrun, Udacity | ML-Udacity | YouTube-Lectures | 2015 |
| 12. | Introduction to Machine Learning | Dhruv Batra, Virginia Tech | ECE-5984 | YouTube-Lectures | 2015 |
| 13. | Statistical Learning - Classification | Ali Ghodsi, University of Waterloo | STAT-441 | YouTube-Lectures | 2015 |
| 14. | Machine Learning Theory | Shai Ben-David, University of Waterloo | None |
YouTube-Lectures | 2015 |
| 15. | Introduction to Machine Learning | Alex Smola, CMU | 10-701 | YouTube-Lectures | S2015 |
| 16. | Statistical Machine Learning | Larry Wasserman, CMU | None |
YouTube-Lectures | S2015 |
| 17. | ML: Supervised Learning | Michael Littman, Charles Isbell, Pushkar Kolhe, GaTech | ML-Udacity | YouTube-Lectures | 2015 |
| 18. | ML: Unsupervised Learning | Michael Littman, Charles Isbell, Pushkar Kolhe, GaTech | ML-Udacity | YouTube-Lectures | 2015 |
| 19. | Advanced Introduction to Machine Learning | Barnabas Poczos and Alex Smola | 10-715 | YouTube-Lectures | F2015 |
| 20. | Machine Learning | Pedro Domingos, UWashington | CSEP-546 | YouTube-Lectures | S2016 |
| 21. | Statistical Machine Learning | Larry Wasserman, CMU | None |
YouTube-Lectures | S2016 |
| 22. | Machine Learning with Large Datasets | William Cohen, CMU | 10-605 | YouTube-Lectures | F2016 |
| 23. | Math Background for Machine Learning | Geoffrey Gordon, CMU | 10-600 |
YouTube-Lectures | F2016 |
| 24. | Statistical Learning - Classification | Ali Ghodsi, University of Waterloo | None |
YouTube-Lectures | 2017 |
| 25. | Machine Learning | Andrew Ng, Stanford University | Coursera-ML | YouTube-Lectures | 2017 |
| 26. | Machine Learning | Roni Rosenfield, CMU | 10-601 | YouTube-Lectures | 2017 |
| 27. | Statistical Machine Learning | Ryan Tibshirani, Larry Wasserman, CMU | 10-702 | YouTube-Lectures | S2017 |
| 28. | Machine Learning for Computer Vision | Fred Hamprecht, Heidelberg University | None |
YouTube-Lectures | F2017 |
| 29. | Math Background for Machine Learning | Geoffrey Gordon, CMU | 10-606 / 10-607 | YouTube-Lectures | F2017 |
| 30. | Data Visualization | Ali Ghodsi, University of Waterloo | None |
YouTube-Lectures | 2017 |
| 31. | Machine Learning for Physicists | Florian Marquardt, Uni Erlangen-Nürnberg | ML4Phy-17 | Lecture-Videos | 2017 |
| 32. | Machine Learning for Intelligent Systems | Kilian Weinberger, Cornell University | CS4780 | YouTube-Lectures | F2018 |
| 33. | Statistical Learning Theory and Applications | Tomaso Poggio, Lorenzo Rosasco, Sasha Rakhlin | 9.520/6.860 | YouTube-Lectures | F2018 |
| 34. | Machine Learning and Data Mining | Mike Gelbart, University of British Columbia | CPSC-340 | YouTube-Lectures | 2018 |
| 35. | Foundations of Machine Learning | David Rosenberg, Bloomberg | FOML | YouTube-Lectures | 2018 |
| 36. | Introduction to Machine Learning | Andreas Krause, ETH Zürich | IntroML | YouTube-Lectures | 2018 |
| 37. | Machine Learning Fundamentals | Sanjoy Dasgupta, UC-San Diego | MLF-slides | YouTube-Lectures | 2018 |
| 38. | Machine Learning | Jordan Boyd-Graber, University of Maryland | CMSC-726 | YouTube-Lectures | 2015-2018 |
| 39. | Machine Learning | Andrew Ng, Stanford University | CS229 | YouTube-Lectures | 2018 |
| 40. | Machine Intelligence | H.R.Tizhoosh, UWaterloo | SYDE-522 | YouTube-Lectures | 2019 |
| 41. | Introduction to Machine Learning | Pascal Poupart, University of Waterloo | CS480/680 | YouTube-Lectures | S2019 |
| 42. | Advanced Machine Learning | Thorsten Joachims, Cornell University | CS-6780 | Lecture-Videos | S2019 |
| 43. | Machine Learning for Structured Data | Matt Gormley, Carnegie Mellon University | 10-418/10-618 | YouTube-Lectures | F2019 |
| 44. | Advanced Machine Learning | Joachim Buhmann, ETH Zürich | ML2-AML | Lecture-Videos | F2019 |
| 45. | Machine Learning for Signal Processing | Vipul Arora, IIT-Kanpur | MLSP | Lecture-Videos | F2019 |
| 46. | Foundations of Machine Learning | Animashree Anandkumar, CalTech | CMS-165 | YouTube-Lectures | 2019 |
| 47. | Machine Learning for Physicists | Florian Marquardt, Uni Erlangen-Nürnberg | None |
Lecture-Videos | 2019 |
| 48. | Applied Machine Learning | Andreas Müller, Columbia University | COMS-W4995 | YouTube-Lectures | 2019 |
| 49. | Fundamentals of Machine Learning over Networks | Hossein Shokri-Ghadikolaei, KTH, Sweden | MLoNs | YouTube-Lectures | 2019 |
| 50. | Foundations of Machine Learning and Statistical Inference | Animashree Anandkumar, CalTech | CMS-165 | YouTube-Lectures | 2020 |
| 51. | Machine Learning | Rebecca Willett and Yuxin Chen, University of Chicago | STAT 37710 / CMSC 35400 | Lecture-Videos | S2020 |
| 52. | Introduction to Machine Learning | Sanjay Lall and Stephen Boyd, Stanford University | EE104/CME107 | YouTube-Lectures | S2020 |
| 53. | Applied Machine Learning | Andreas Müller, Columbia University | COMS-W4995 | YouTube-Lectures | S2020 |
| 54. | Statistical Machine Learning | Ulrike von Luxburg, Eberhard Karls Universität Tübingen | Stat-ML | YouTube-Lectures | SS2020 |
| 55. | Probabilistic Machine Learning | Philipp Hennig, Eberhard Karls Universität Tübingen | Prob-ML | YouTube-Lectures | SS2020 |
| 56. | Machine Learning | Sarath Chandar, PolyMTL, UdeM, Mila | INF8953CE | YouTube-Lectures | F2020 |
| 57. | Machine Learning | Erik Bekkers, Universiteit van Amsterdam | UvA-ML | YouTube-Lectures | F2020 |
| 58. | Neural Networks for Signal Processing | Shayan Srinivasa Garani, Indian Institute of Science | NN4SP | YouTube-Lectures | F2020 |
| 59. | Introduction to Machine Learning | Dmitry Kobak, Universität Klinikum Tübingen | None |
YouTube-Lectures | 2020 |
| 60. | Machine Learning (PRML) | Erik J. Bekkers, Universiteit van Amsterdam | UvAML-1 | YouTube-Lectures | 2020 |
| 61. | Machine Learning with Kernel Methods | Julien Mairal and Jean-Philippe Vert, Inria/ENS Paris-Saclay, Google | ML-Kernels | YouTube-Lectures | S2021 |
| 62. | Continual Learning | Vincenzo Lomonaco, Università di Pisa | ContLearn'21 | YouTube-Lectures | 2021 |
| 63. | Causality | Christina Heinze-Deml, ETH Zurich | Causal'21 | YouTube-Lectures | 2021 |
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| 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 | 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 | Data Efficient Reinforcement Learning | Lots of Legends, Canary Islands | DERL-17 | YouTube-Lectures | 2017 |
| 10. | Deep Reinforcement Learning | Sergey Levine, UC Berkeley | CS-294-112 | YouTube-Lectures | 2018 |
| 11. | Reinforcement Learning | Pascal Poupart, University of Waterloo | CS-885 | YouTube-Lectures | 2018 |
| 12. | Deep Reinforcement Learning and Control | Katerina Fragkiadaki and Tom Mitchell, CMU | 10-703 | YouTube-Lectures | 2018 |
| 13. | Reinforcement Learning and Optimal Control | Dimitri Bertsekas, Arizona State University | RLOC | Lecture-Videos | 2019 |
| 14. | Reinforcement Learning | Emma Brunskill, Stanford University | CS 234 | YouTube-Lectures | 2019 |
| 15. | Reinforcement Learning Day | Lots of Legends, Microsoft Research, New York | RLD-19 | YouTube-Lectures | 2019 |
| 16. | New Directions in Reinforcement Learning and Control | Lots of Legends, IAS, Princeton University | NDRLC-19 | YouTube-Lectures | 2019 |
| 17. | Deep Reinforcement Learning | Sergey Levine, UC Berkeley | CS 285 | YouTube-Lectures | F2019 |
| 18. | Deep Multi-Task and Meta Learning | Chelsea Finn, Stanford University | CS 330 | YouTube-Lectures | F2019 |
| 19. | RL-Theory Seminars | Lots of Legends, Earth | RL-theory-sem | YouTube-Lectures | 2020 - |
| 20. | Deep Reinforcement Learning | Sergey Levine, UC Berkeley | CS 285 | YouTube-Lectures | F2020 |
| 21. | Introduction to Reinforcement Learning | Amir-massoud Farahmand, Vector Institute, University of Toronto | RL-intro | YouTube-Lectures | S2021 |
| 22. | Reinforcement Learning | Antonio Celani and Emanuele Panizon, International Centre for Theoretical Physics | None |
YouTube-Lectures | 2021 |
| 23. | Computational Sensorimotor Learning | Pulkit Agrawal, MIT-CSAIL | 6.884-CSL | YouTube-Lectures | S2021 |
| 24. | Reinforcement Learning | Dimitri P. Bertsekas, ASU/MIT | RL-21 | YouTube-Lectures | S2021 |
| 25. | Reinforcement Learning | Sarath Chandar, École Polytechnique de Montréal | INF8953DE | YouTube-Lectures | F2021 |
| 26. | Deep Reinforcement Learning | Sergey Levine, UC Berkeley | CS 285 | YouTube-Lectures | F2021 |
| 27. | Reinforcement Learning Lecture Series | Lots of Legends, DeepMind & UC London | RL-series | YouTube-Lectures | 2021 |
| 28. | Reinforcement Learning | Dimitri P. Bertsekas, ASU/MIT | RL-22 | YouTube-Lectures | S2022 |
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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 |
| 6. | Probabilistic Graphical Models | Eric Xing, CMU | 10-708 | Lecture-Videos YouTube-Lectures |
S2019 |
| 7. | Probabilistic Graphical Models | Eric Xing, CMU | 10-708 | YouTube-Lectures | S2020 |
| 8. | Uncertainty Modeling in AI | Gim Hee Lee, National University of Singapura (NUS) | CS 5340 - CH, CS 5340-NB | YouTube-Lectures | 2020-21 |
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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 |
| 3. | Deep Learning and Bayesian Methods | Lots of Legends, HSE Moscow | DLBM-SS | YouTube-Lectures | 2018 |
| 4. | Deep Learning and Bayesian Methods | Lots of Legends, HSE Moscow | DLBM-SS | YouTube-Lectures | 2019 |
| 5. | Nordic Probabilistic AI | Lots of Legends, NTNU, Trondheim | ProbAI | YouTube-Lectures | 2019 |
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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 |
| 6. | Seminar on AI in Healthcare | Lots of Legends, Stanford | CS 522 | YouTube-Lectures | 2018 |
| 7. | Machine Learning for Healthcare | David Sontag, Peter Szolovits, CSAIL MIT | MLHC-19 MIT 6.S897 |
YouTube-Lectures | S2019 |
| 8. | Deep Learning and Medical Applications | Lots of Legends, IPAM, UCLA | DLM-20 | Lecture-Videos | 2020 |
| 9. | Stanford Symposium on Artificial Intelligence in Medicine and Imaging | Lots of Legends, Stanford AIMI | AIMI-20 | YouTube-Lectures | 2020 |
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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. | Analysis of Networks: Mining and Learning with Graphs | Jure Leskovec, Stanford University | CS224W | Lecture-Videos | 2018 |
| 6. | Machine Learning with Graphs | Jure Leskovec, Stanford University | CS224W | YouTube-Lectures | 2019 |
| 7. | Geometry and Learning from Data in 3D and Beyond -Geometry and Learning from Data Tutorials | Lots of Legends, IPAM UCLA | GLDT | Lecture-Videos | 2019 |
| 8. | Geometry and Learning from Data in 3D and Beyond - Geometric Processing | Lots of Legends, IPAM UCLA | GeoPro | Lecture-Videos | 2019 |
| 9. | Geometry and Learning from Data in 3D and Beyond - Shape Analysis | Lots of Legends, IPAM UCLA | Shape-Analysis | Lecture-Videos | 2019 |
| 10. | Geometry and Learning from Data in 3D and Beyond - Geometry of Big Data | Lots of Legends, IPAM UCLA | Geo-BData | Lecture-Videos | 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 | Lecture-Videos | 2019 |
| 12. | Israeli Geometric Deep Learning | Lots of Legends, Israel | iGDL-20 | Lecture-Videos | 2020 |
| 13. | Machine Learning for Graphs and Sequential Data | Stephan Günnemann, Technische Universität München (TUM) | MLGS-20 | Lecture-Videos | S2020 |
| 14. | Machine Learning with Graphs | Jure Leskovec, Stanford | CS224W | YouTube-Lectures | W2021 |
| 15. | Geometric Deep Learning - AMMI | Lots of Legends, Virtual | GDL-AMMI | YouTube-Lectures | 2021 |
| 16. | Summer School on Geometric Deep Learning - | Lots of Legends, DTU, DIKU & AAU | GDL- DTU, DIKU & AAU | Lecture-Videos | 2021 |
| 17. | Graph Neural Networks | Alejandro Ribeiro, University of Pennsylvania | ESE 514 | YouTube-Lectures | F2021 |
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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 |
| 10. | Natural Language Understanding | Bill MacCartney and Christopher Potts | CS224U | YouTube-Lectures | S2019 |
| 11. | Neural Networks for Natural Language Processing | Graham Neubig, Carnegie Mellon University | CS 11-747 | YouTube-Lectures | S2020 |
| 12. | Advanced Natural Language Processing | Mohit Iyyer, UMass Amherst | CS 685 | YouTube-Lectures | F2020 |
| 13. | Machine Translation | Philipp Koehn, Johns Hopkins University | EN 601.468/668 | YouTube-Lectures | F2020 |
| 14. | Neural Networks for NLP | Graham Neubig, Carnegie Mellon University | CS 11-747 | YouTube-Lectures | 2021 |
| 15. | Deep Learning for Natural Language Processing | Kyunghyun Cho, New York University | DS-GA 1011 | YouTube-Lectures | F2021 |
| 16. | Natural Language Processing with Deep Learning | Chris Manning, Stanford University | CS224n | YouTube-Lectures | 2021 |
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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. | Automatic Speech Recognition | Samudra Vijaya K, TIFR | None |
YouTube-Videos | 2016 |
| 4. | Speech and Audio in the Northeast | Many Legends, Google NYC | SANE-17 | YouTube-Videos | 2017 |
| 5. | 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 Multidimensional Signal Processing - (classical) | William Hoff, Colorado School of Mines | CSCI 510/EENG 510 | YouTube-Lectures | 2012 |
| 4. | Computer Vision - (classical) | William Hoff, Colorado School of Mines | CSCI 512/EENG 512 | YouTube-Lectures | 2012 |
| 5. | Image and Video Processing: From Mars to Hollywood with a Stop at the Hospital | Guillermo Sapiro, Duke University | None |
YouTube-Videos | 2013 |
| 6. | Multiple View Geometry (classical) | Daniel Cremers, Technische Universität München | mvg | YouTube-Lectures | 2013 |
| 7. | Mathematical Methods for Robotics, Vision, and Graphics | Justin Solomon, Stanford University | CS-205A | YouTube-Lectures | 2013 |
| 8. | Computer Vision - (classical) | Mubarak Shah, UCF | CAP-5415 | YouTube-Lectures | 2014 |
| 9. | Computer Vision for Visual Effects (classical) | Rich Radke, Rensselaer Polytechnic Institute | ECSE-6969 | YouTube-Lectures | S2014 |
| 10. | Autonomous Navigation for Flying Robots | Juergen Sturm, Technische Universität München | Autonavx | YouTube-Lectures | 2014 |
| 11. | SLAM - Mobile Robotics | Cyrill Stachniss, Universitaet Freiburg | RobotMapping | YouTube-Lectures | 2014 |
| 12. | Computational Photography | Irfan Essa, David Joyner, Arpan Chakraborty | CP-Udacity | YouTube-Lectures | 2015 |
| 13. | Introduction to Digital Image Processing | Rich Radke, Rensselaer Polytechnic Institute | ECSE-4540 | YouTube-Lectures | S2015 |
| 14. | Lectures on Digital Photography | Marc Levoy, Stanford/Google Research | LoDP | YouTube-Lectures | 2016 |
| 15. | Introduction to Computer Vision (foundation) | Aaron Bobick, Irfan Essa, Arpan Chakraborty | CV-Udacity | YouTube-Lectures | 2016 |
| 16. | Computer Vision | Syed Afaq Ali Shah, University of Western Australia | None |
YouTube-Lectures | 2016 |
| 17. | Photogrammetry I & II | Cyrill Stachniss, University of Bonn | PG-I&II | YouTube-Lectures | 2016 |
| 18. | Deep Learning for Computer Vision | UPC Barcelona | DLCV-16 DLCV-17 DLCV-18 |
YouTube-Lectures | 2016-2018 |
| 19. | Convolutional Neural Networks | Andrew Ng, Stanford University | DeepLearning.AI | YouTube-Lectures | 2017 |
| 20. | Variational Methods for Computer Vision | Daniel Cremers, Technische Universität München | VMCV | YouTube-Lectures | 2017 |
| 21. | Winter School on Computer Vision | Lots of Legends, Israel Institute for Advanced Studies | WS-CV | YouTube-Lectures | 2017 |
| 22. | Deep Learning for Visual Computing | Debdoot Sheet, IIT-Kgp | Nptel Notebooks | YouTube-Lectures | 2018 |
| 23. | The Ancient Secrets of Computer Vision | Joseph Redmon, Ali Farhadi | TASCV ; TASCV-UW | YouTube-Lectures | 2018 |
| 24. | Modern Robotics | Kevin Lynch, Northwestern Robotics | modern-robot | YouTube-Lectures | 2018 |
| 25. | Digial Image Processing | Alex Bronstein, Technion | CS236860 | YouTube-Lectures | 2018 |
| 26. | Mathematics of Imaging - Variational Methods and Optimization in Imaging | Lots of Legends, Institut Henri Poincaré | Workshop-1 | YouTube-Lectures | 2019 |
| 27. | Deep Learning for Video | Xavier Giró, UPC Barcelona | deepvideo | YouTube-Lectures | 2019 |
| 28. | Statistical modeling for shapes and imaging | Lots of Legends, Institut Henri Poincaré, Paris | workshop-2 | YouTube-Lectures | 2019 |
| 29. | Imaging and machine learning | Lots of Legends, Institut Henri Poincaré, Paris | workshop-3 | YouTube-Lectures | 2019 |
| 30. | Computer Vision | Jayanta Mukhopadhyay, IIT Kgp | CV-nptel | YouTube-Lectures | 2019 |
| 31. | Deep Learning for Computer Vision | Justin Johnson, UMichigan | EECS 498-007 | Lecture-Videos YouTube-Lectures |
2019 |
| 32. | Sensors and State Estimation 2 | Cyrill Stachniss, University of Bonn | None |
YouTube-Lectures | S2020 |
| 33. | Computer Vision III: Detection, Segmentation and Tracking | Laura Leal-Taixé, TU München | CV3DST | YouTube-Lectures | S2020 |
| 34. | Advanced Deep Learning for Computer Vision | Laura Leal-Taixé and Matthias Nießner, TU München | ADL4CV | YouTube-Lectures | S2020 |
| 35. | Computer Vision: Foundations | Fred Hamprecht, Universität Heidelberg | CVF | YouTube-Lectures | SS2020 |
| 36. | MIT Vision Seminar | Lots of Legends, MIT | MIT-Vision | YouTube-Lectures | 2015-now |
| 37. | TUM AI Guest Lectures | Lots of Legends, Technische Universität München | TUM-AI | YouTube-Lectures | 2020 - now |
| 38. | Seminar on 3D Geometry & Vision | Lots of Legends, Virtual | 3DGV seminar | YouTube-Lectures | 2020 - now |
| 39. | Event-based Robot Vision | Guillermo Gallego, Technische Universität Berlin | EVIS-SS20 | YouTube-Lectures | 2020 - now |
| 40. | Deep Learning for Computer Vision | Vineeth Balasubramanian, IIT Hyderabad | DL-CV'20 | YouTube-Lectures | 2020 |
| 41. | Deep Learning for Visual Computing | Peter Wonka, KAUST, SA | NOne |
YouTube-Lectures | 2020 |
| 42. | Computer Vision | Yogesh Rawat, University of Central Florida | CAP5415-CV | YouTube-Lectures | F2020 |
| 43. | Multimedia Signal Processing | Mark Hasegawa-Johnson, UIUC | ECE-417 MSP | Lecture Videos | F2020 |
| 44. | Computer Vision | Andreas Geiger, Universität Tübingen | Comp.Vis | YouTube-Lectures | S2021 |
| 45. | 3D Computer Vision | Lee Gim Hee, National Univeristy of Singapura | None |
YouTube-Lectures | 2021 |
| 46. | Deep Learning for Computer Vision: Fundamentals and Applications | T. Dekel et al., Weizmann Institute of Science | DL4CV | YouTube-Lectures | S2021 |
| 47. | Current Topics in ML Methods in 3D and Geometric Deep Learning | Animesh Garg & others, University of Toronto | CSC 2547 | YouTube-Lectures | 2021 |
| 48. | First Principles of Computer Vision | Shree K. Nayar, Columbia University | FPCV | YouTube-Lectures | 2021 |
| 49. | Self-Driving Cars | Andreas Geiger, Universität Tübingen | SDC'21 | YouTube-Lectures | W2021 |
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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 | Graduate Summer School: Computer Vision | Lots of Legends, IPAM-UCLA | GSS-CV | Video-Lectures | 2013 |
| 5. | Machine Learning Summer School | Lots of Legends, Reykjavik University | MLSS-14 | YouTube-Lectures | 2014 |
| 6. | Machine Learning Summer School | Lots of Legends, Pittsburgh | MLSS-14 | YouTube-Lectures | 2014 |
| 7. | Deep Learning Summer School | Lots of Legends, Université de Montréal | DLSS-15 | YouTube-Lectures | 2015 |
| 8. | Biomedical Image Analysis Summer School | Lots of Legends, CentraleSupelec, Paris | None |
YouTube-Lectures | 2015 |
| 9. | Mathematics of Signal Processing | Lots of Legends, Hausdorff Institute for Mathematics | SigProc | YouTube-Lectures | 2016 |
| 10. | Microsoft Research - Machine Learning Course | S V N Vishwanathan and Prateek Jain MS-Research | None |
YouTube-Lectures | 2016 |
| 11. | Deep Learning Summer School | Lots of Legends, Université de Montréal | DL-SS-16 | YouTube-Lectures | 2016 |
| 12. | Lisbon Machine Learning School | Lots of Legends, Instituto Superior Técnico, Portugal | LxMLS-16 | YouTube-Lectures | 2016 |
| 13. | Machine Learning Advances and Applications Seminar | Lots of Legends, Fields Institute, University of Toronto | MLAAS-16 | YouTube-Lectures Video-Lectures |
2016-2017 |
| 14. | Machine Learning Advances and Applications Seminar | Lots of Legends, Fields Institute, University of Toronto | MLAAS-17 | Video Lectures | 2017-2018 |
| 15. | Machine Learning Summer School | Lots of Legends, MPI-IS Tübingen | MLSS-17 | YouTube-Lectures | 2017 |
| 16. | Representation Learning | Lots of Legends, Simons Institute | RepLearn | YouTube-Lectures | 2017 |
| 17. | Foundations of Machine Learning | Lots of Legends, Simons Institute | ML-BootCamp | YouTube-Lectures | 2017 |
| 18. | Optimization, Statistics, and Uncertainty | Lots of Legends, Simons Institute | Optim-Stats | YouTube-Lectures | 2017 |
| 19. | Deep Learning: Theory, Algorithms, and Applications | Lots of Legends, TU-Berlin | DL: TAA | YouTube-Lectures | 2017 |
| 20. | Deep Learning and Reinforcement Learning Summer School | Lots of Legends, Université de Montréal | DLRL-2017 | Lecture-videos | 2017 |
| 21. | Statistical Physics Methods in Machine Learning | Lots of Legends, International Centre for Theoretical Sciences, TIFR | SPMML | YouTube-Lectures | 2017 |
| 22. | Lisbon Machine Learning School | Lots of Legends, Instituto Superior Técnico, Portugal | LxMLS-17 | YouTube-Lectures | 2017 |
| 23. | Interactive Learning | Lots of Legends, Simons Institute, Berkeley | IL-2017 | YouTube-Lectures | 2017 |
| 24. | Computational Challenges in Machine Learning | Lots of Legends, Simons Institute, Berkeley | CCML-17 | YouTube-Lectures | 2017 |
| 25. | Foundations of Data Science | Lots of Legends, Simons Institute | DS-BootCamp | YouTube-Lectures | 2018 |
| 26. | Deep Learning and Bayesian Methods | Lots of Legends, HSE Moscow | DLBM-SS | YouTube-Lectures | 2018 |
| 27. | New Deep Learning Techniques | Lots of Legends, IPAM UCLA | IPAM-Workshop | YouTube-Lectures | 2018 |
| 28. | Deep Learning and Reinforcement Learning Summer School | Lots of Legends, University of Toronto | DLRL-2018 | Lecture-videos | 2018 |
| 29. | Machine Learning Summer School | Lots of Legends, Universidad Autónoma de Madrid, Spain | MLSS-18 | YouTube-Lectures Course-videos |
2018 |
| 30. | Theoretical Basis of Machine Learning | Lots of Legends, International Centre for Theoretical Sciences, TIFR | TBML-18 | Lecture-Videos YouTube-Videos |
2018 |
| 31. | Polish View on Machine Learning | Lots of Legends, Warsaw | PLinML-18 | YouTube-Videos | 2018 |
| 32. | Big Data Analysis in Astronomy | Lots of Legends, Tenerife | BDAA-18 | YouTube-Lectures | 2018 |
| 33. | Machine Learning Advances and Applications Seminar | Lots of Legends, Fields Institute, University of Toronto | MLASS | Video Lectures | 2018-2019 |
| 34. | MIFODS- ML, Stats, ToC seminar | Lots of Legends, MIT | MIFODS-seminar | Lecture-videos | 2018-2019 |
| 35. | Learning Machines Seminar Series | Lots of Legends, Cornell Tech | LMSS | YouTube-Lectures | 2018-now |
| 36. | Machine Learning Summer School | Lots of Legends, South Africa | MLSS'19 | YouTube-Lectures | 2019 |
| 37. | Deep Learning Boot Camp | Lots of Legends, Simons Institute, Berkeley | DLBC-19 | YouTube-Lectures | 2019 |
| 38. | Frontiers of Deep Learning | Lots of Legends, Simons Institute, Berkeley | FoDL-19 | YouTube-Lectures | 2019 |
| 39. | Mathematics of data: Structured representations for sensing, approximation and learning | Lots of Legends, The Alan Turing Institute, London | MoD-19 | YouTube-Lectures | 2019 |
| 40. | Deep Learning and Bayesian Methods | Lots of Legends, HSE Moscow | DLBM-SS | YouTube-Lectures | 2019 |
| 41. | The Mathematics of Deep Learning and Data Science | Lots of Legends, Isaac Newton Institute, Cambridge | MoDL-DS | Lecture-Videos | 2019 |
| 42. | Geometry of Deep Learning | Lots of Legends, MSR Redmond | GoDL | YouTube-Lectures | 2019 |
| 43. | Deep Learning for Science School | Many folks, LBNL, Berkeley | DLfSS | YouTube-Lectures | 2019 |
| 44. | Emerging Challenges in Deep Learning | Lots of Legends, Simons Institute, Berkeley | ECDL | YouTube-Lectures | 2019 |
| 45. | Full Stack Deep Learning | Pieter Abbeel and many others, UC Berkeley | FSDL-M19 | YouTube-Lectures-Day-1 Day-2 |
2019 |
| 46. | Algorithmic and Theoretical aspects of Machine Learning | Lots of legends, IIIT-Bengaluru | ACM-ML nptel |
YouTube-Lectures | 2019 |
| 47. | Deep Learning and Reinforcement Learning Summer School | Lots of Legends, AMII, Edmonton, Canada | DLRL-2019 | YouTube-Lectures | 2019 |
| 48. | Mathematics of Machine Learning - Summer Graduate School | Lots of Legends, University of Washington | MoML-SGS, MoML-SS | YouTube-Lectures | 2019 |
| 49. | Workshop on Theory of Deep Learning: Where next? | Lots of Legends, IAS, Princeton University | WTDL | YouTube-Lectures | 2019 |
| 50. | Computational Vision Summer School | Lots of Legends, Black Forest, Germany | CVSS-2019 | YouTube-Lectures | 2019 |
| 51. | Learning under complex structure | Lots of Legends, MIT | LUCS | YouTube-Lectures | 2020 |
| 52. | Machine Learning Summer School | Lots of Legends, MPI-IS Tübingen (virtual) | MLSS | YouTube-Lectures | SS2020 |
| 53. | Eastern European Machine Learning Summer School | Lots of Legends, Kraków, Poland (virtual) | EEML | YouTube-Lectures | S2020 |
| 54. | Lisbon Machine Learning Summer School | Lots of Legends, Lisbon, Portugal (virtual) | LxMLS | YouTube-Lectures | S2020 |
| 55. | Workshop on New Directions in Optimization, Statistics and Machine Learning | Lots of Legends, Institute of Advanced Study, Princeton | ML-Opt new dir. | YouTube-Lectures | 2020 |
| 56. | Mediterranean Machine Learning School | Lots of Legends, Italy (virtual) | M2L-school | YouTube-Lectures | 2021 |
| 57. | Mathematics of Machine Learning - One World Seminar | Lots of Legends, Virtual | 1W-ML | YouTube-Lectures | 2020 - now |
| 58. | Deep Learning Theory Summer School | Lots of Legends, Princeton University (virtual) | DLT'21 | YouTube-Lectures | 2021 |
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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
⬜ Section on Computer Graphics with ML/DL focus
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- Montreal.AI
- UPC-DLAI-2018
- UPC-DLAI-2019
- www.hashtagtechgeek.com
- UPC-Barcelona, IDL-2020
- UPC-DLAI-2020
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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.
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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 🙏
Vielen lieben Dank! 💙
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