"Read enough so you start developing intuitions and then trust your intuitions and go for it!"
Prof. Geoffrey Hinton, University of Toronto
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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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