/deep-learning-drizzle

Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!

🎈 🎉 Deep Learning Drizzle 🎊 🎈

📚 "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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Contents

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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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🎉 Deep Learning (Deep Neural Networks) 🎊 🎈

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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- 2019
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. Deep Learning Andreas Geiger, Universität Tübingen DL-UT YouTube-Lectures W20/21
68. Fundamentals of Deep Learning Terence Parr and Yannet Interian, University of San Francisco DL-Fundamentals YouTube-Lectures S2021
69. Full Stack Deep Learning Pieter Abbeel, Sergey Karayev, UC Berkeley FS-DL YouTube-Lectures S2021
70. Deep Learning: Designing, Visualizing, and Understanding DNNs Sergey Levine, UC Berkeley CS 182 YouTube-Lectures S2021
71. Deep Learning in the Life Sciences Manolis Kellis, MIT 6.874 YouTube-Lectures S2021
72. Introduction to Deep Learning and Generative Models Sebastian Raschka, University of Wisconsin-Madison Stat 453 YouTube-Lectures S2021
73. Applied Deep Learning Alexander Pacha, TU Wien None YouTube-Lectures 2020-2021

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💘 Machine Learning Fundamentals 🌀 💥

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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

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💘 Optimization for Machine Learning 🌀 💥

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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. 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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💘 General Machine Learning 🌀 💥

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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. Applied Machine Learning Andreas Müller, Columbia University COMS-W4995 YouTube-Lectures S2020
52. Statistical Machine Learning Ulrike von Luxburg, Eberhard Karls Universität Tübingen Stat-ML YouTube-Lectures SS2020
53. Probabilistic Machine Learning Philipp Hennig, Eberhard Karls Universität Tübingen Prob-ML YouTube-Lectures SS2020
54. Machine Learning Sarath Chandar, PolyMTL, UdeM, Mila INF8953CE YouTube-Lectures F2020
55. Machine Learning Erik Bekkers, Universiteit van Amsterdam UvA-ML YouTube-Lectures F2020
56. Neural Networks for Signal Processing Shayan Srinivasa Garani, Indian Institute of Science NN4SP YouTube-Lectures F2020
57. Introduction to Machine Learning Dmitry Kobak, Universität Klinikum Tübingen None YouTube-Lectures 2020
58. Machine Learning with Kernel Methods Julien Mairal and Jean-Philippe Vert, Inria/ENS Paris-Saclay, Google ML-Kernels YouTube-Lectures S2021

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🎈 Reinforcement Learning ♨️ 🎮

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S.No Course Name University/Instructor(s) Course Webpage Video Lectures Year
1. Short Course on Reinforcement Learning Satinder Singh, UMichigan None YouTube-Lectures 2011
2. Approximate Dynamic Programming Dimitri P. Bertsekas, MIT Lecture-Slides YouTube-Lectures 2014
3. Introduction to Reinforcement Learning David Silver, DeepMind UCL-RL YouTube-Lectures 2015
4. Reinforcement Learning Charles Isbell, Chris Pryby, GaTech; Michael Littman, Brown RL-Udacity YouTube-Lectures 2015
5. Reinforcement Learning Balaraman Ravindran, IIT Madras RL-IITM YouTube-Lectures 2016
6. Deep Reinforcement Learning Sergey Levine, UC Berkeley CS-294 YouTube-Lectures S2017
7. Deep Reinforcement Learning Sergey Levine, UC Berkeley CS-294 YouTube-Lectures F2017
8. Deep RL Bootcamp Many legends, UC Berkeley Deep-RL YouTube-Lectures 2017
9 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 CS234 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 CS285 YouTube-Lectures F2019
18. Deep Multi-Task and Meta Learning Chelsea Finn, Stanford University CS330 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 CS285 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

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📢 Probabilistic Graphical Models ✨

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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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🎲 Bayesian Deep Learning ♠️ 💎

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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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🎥 Medical Imaging 📷 📹

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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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🎉 Graph Neural Networks (Geometric DL) 🎊 🎈

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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

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🌺 Natural Language Processing 🌸 💖

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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

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🗣️ Automatic Speech Recognition 💬 💭

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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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🔥 Modern Computer Vision 📸 🎥

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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

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🌟 Boot Camps or Summer Schools 🍁

➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖

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

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🐦 Bird's Eye view of A(G)I 🦅

➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖

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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To-Do 🏃

➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖

⬜ 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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Around the Web 🌏

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Contributions 🙏

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.

Vielen lieben Dank! 💙

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