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

Contents

Deep Learning (Deep Neural Networks) Probabilistic Graphical Models
Machine Learning Fundamentals Natural Language Processing
Optimization for Machine Learning Automatic Speech Recognition
General Machine Learning Modern Computer Vision
Reinforcement Learning Boot Camps or Summer Schools
Graph Neural Networks Bird's-eye view of Artificial Intelligence



Deep Learning (Deep Neural Networks)

S.No Course Name University/Instructor(s) Course WebPage Lecture Videos Year
1. Neural Networks for Machine Learning Geoffrey Hinton, University of Toronto Lecture-Slides
CSC321-tijmen
YouTube-Lectures
UofT-mirror
2012
2014
2. Neural Networks Demystified Stephen Welch, Welch Labs Suppl. Code YouTube-Lectures 2014
3. Deep Learning at Oxford Nando de Freitas, Oxford University Oxford-ML YouTube-Lectures 2015
4. Deep Learning for Perception Dhruv Batra, Virginia Tech ECE-6504 YouTube-Lectures 2015
5. Deep Learning Ali Ghodsi, University of Waterloo STAT-946 YouTube-Lectures F2015
6. CS231n: CNNs for Visual Recognition Andrej Karpathy, Stanford University CS231n None 2015
7. CS224d: Deep Learning for NLP Richard Socher, Stanford University CS224d YouTube-Lectures 2015
8. Bay Area Deep Learning Many legends, Stanford None YouTube-Lectures 2016
9. CS231n: CNNs for Visual Recognition Andrej Karpathy, Stanford University CS231n YouTube-Lectures
(Academic Torrent)
2016
10. Neural Networks Hugo Larochelle, Université de Sherbrooke Neural-Networks YouTube-Lectures
(Academic Torrent)
2016
11. CS224d: Deep Learning for NLP Richard Socher, Stanford University CS224d YouTube-Lectures
(Academic Torrent)
2016
12. CS224n: NLP with Deep Learning Richard Socher, Stanford University CS224n YouTube-Lectures 2017
13. CS231n: CNNs for Visual Recognition Justin Johnson, Stanford University CS231n YouTube-Lectures
(Academic Torrent)
2017
14. Topics in Deep Learning Ruslan Salakhutdinov, CMU 10707 YouTube-Lectures F2017
15. Deep Learning Crash Course Leo Isikdogan, UT Austin None YouTube-Lectures 2017
16. Deep Learning Andrew Ng, Stanford University CS230 None 2018
17. UvA Deep Learning Efstratios Gavves, University of Amsterdam UvA-DLC Lecture-Videos 2018
18. Advanced Deep Learning and Reinforcement Learning Many legends, DeepMind None YouTube-Lectures 2018
19. Machine Learning Peter Bloem, Vrije Universiteit Amsterdam MLVU YouTube-Lectures 2018
20. Deep Learning Francois Fleuret, EPFL EE-59 Video-Lectures 2018
21. Introduction to Deep Learning Alexander Amini, Harini Suresh and others, MIT 6.S191 YouTube-Lectures
2017-version
2017- 2019
22. Deep Learning for Self-Driving Cars Lex Fridman, MIT 6.S094 YouTube-Lectures 2017-2018
23. Introduction to Deep Learning Bhiksha Raj and many others, CMU 11-485/785 YouTube-Lectures S2018
24. Introduction to Deep Learning Bhiksha Raj and many others, CMU 11-485/785 YouTube-Lectures Recitation-Inclusive F2018
25. Deep Learning Specialization Andrew Ng, Stanford DL.AI YouTube-Lectures 2017-2018
26. Deep Learning Ali Ghodsi, University of Waterloo STAT-946 YouTube-Lectures F2017
27. Deep Learning Mitesh Khapra, IIT-Madras CS7015 YouTube-Lectures 2018
28. Deep Learning for AI UPC Barcelona DLAI-2017
DLAI-2018
YouTube-Lectures 2017-2018
29. Deep Learning Alex Bronstein and Avi Mendelson, Technion CS236605 YouTube-Lectures 2018
30. MIT Deep Learning Many Researchers, Lex Fridman, MIT 6.S094, 6.S091, 6.S093 YouTube-Lectures 2019
31. Deep Learning Book companion videos Ian Goodfellow and others DL-book slides YouTube-Lectures 2017
33. Theories of Deep Learning Many Legends, Stanford Stats-385 YouTube-Lectures
(first 10 lectures)
F2017
34. Neural Networks Grant Sanderson None YouTube-Lectures 2017-2018
35. CS230: Deep Learning Andrew Ng, Kian Katanforoosh, Stanford CS230 YouTube-Lectures A2018
36. Introduction to Deep Learning Alex Smola, UC Berkeley Stat-157 YouTube-Lectures S2019
37. Deep Unsupervised Learning Pieter Abbeel, UC Berkeley CS294-158 YouTube-Lectures S2019
38. Machine Learning Peter Bloem, Vrije Universiteit Amsterdam MLVU YouTube-Lectures 2019
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Machine Learning Fundamentals

S.No Course Name University/Instructor(s) Course Webpage Video Lectures Year
1. Linear Algebra Gilbert Strang, MIT 18.06 SC YouTube-Lectures 2011
2. Probability Primer Jeffrey Miller, Brown University mathematical monk YouTube-Lectures 2011
3. Information Theory, Pattern Recognition, and Neural Networks David Mackay, University of Cambridge ITPRNN YouTube-Lectures 2012
4. Probability and Statistics Michel van Biezen None YouTube-Lectures 2015
5. Linear Algebra: An in-depth Introduction Pavel Grinfeld None Part-1
Part-2
Part-3
Part-4
2015- 2017
6. Multivariable Calculus Grant Sanderson, Khan Academy None YouTube-Lectures 2016
7. Essence of Linear Algebra Grant Sanderson None YouTube-Lectures 2016
8. Essence of Calculus Grant Sanderson None YouTube-Lectures 2017-2018
9. Mathematics for Machine Learning (Linear Algebra, Calculus) David Dye, Samuel Cooper, and Freddie Page, IC-London MML YouTube-Lectures 2018
10. Multivariable Calculus S.K. Gupta and Sanjeev Kumar, IIT-Roorkee MVC YouTube-Lectures 2018
11. Engineering Probability Rich Radke, Rensselaer Polytechnic Institute None YouTube-Lectures 2018
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Optimization for Machine Learning

S.No Course Name University/Instructor(s) Course Webpage Video Lectures Year
1. Convex Optimization Stephen Boyd, Stanford University ee364a YouTube-Lectures 2008
2. Introduction to Optimization Michael Zibulevsky, Technion CS-236330 YouTube-Lectures 2009
3. Optimization for Machine Learning S V N Vishwanathan, Purdue University None YouTube-Lectures 2011
4. Optimization Geoff Gordon & Ryan Tibshirani, CMU 10-725 YouTube-Lectures 2012
5. Convex Optimization Joydeep Dutta, IIT-Kanpur cvx-nptel YouTube-Lectures 2013
6. Algorithmic Aspects of Machine Learning Ankur Moitra, MIT 18.409-AAML YouTube-Lectures S2015
7. Advanced Algorithms Ankur Moitra, MIT 6.854-AA YouTube-Lectures S2016
8 Introduction to Optimization Michael Zibulevsky, Technion None YouTube-Lectures 2016
9. Convex Optimization Ryan Tibshirani, CMU cvx-opt YouTube-Lectures F2018
10. Modern Algorithmic Optimization Yurii Nesterov, UCLouvain None YouTube-Lectures 2018
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General Machine Learning

S.No Course Name University/Instructor(s) Course Webpage Video Lectures Year
1. CS229: Machine Learning Andrew Ng, Stanford University CS229-old
CS229-new
YouTube-Lectures 2007
2. Machine Learning Jeffrey Miller, Brown University mathematical monk YouTube-Lectures 2011
3. Machine Learning and Data Mining Nando de Freitas, University of British Columbia CPSC-340 YouTube-Lectures 2012
4. Learning from Data Yaser Abu-Mostafa, CalTech CS156 YouTube-Lectures 2012
5. Machine Learning Rudolph Triebel, Technische Universität München Machine Learning YouTube-Lectures 2013
6. Introduction to Machine Learning Alex Smola, CMU 10-701 YouTube-Lectures 2013
7. Introduction to Machine Learning Alex Smola and Geoffrey Gordon, CMU 10-701x YouTube-Lectures 2013
8. Pattern Recognition Sukhendu Das, IIT-M and C.A. Murthy, ISI-Calcutta PR-NPTEL YouTube-Lectures 2014
9. An Introduction to Statistical Learning with Applications in R Trevor Hastie and Robert Tibshirani, Stanford stat-learn
R-bloggers
YouTube-Lectures 2014
10. Introduction to Machine Learning Katie Malone, Sebastian Thrun, Udacity ML-Udacity YouTube-Lectures 2015
11. Introduction to Machine Learning Dhruv Batra, Virginia Tech ECE-5984 YouTube-Lectures 2015
12. Statistical Learning - Classification Ali Ghodsi, University of Waterloo STAT-441 YouTube-Lectures 2015
13. Machine Learning Theory Shai Ben-David, University of Waterloo None YouTube-Lectures 2015
14. Introduction to Machine Learning Alex Smola, CMU 10-701 YouTube-Lectures S2015
15. ML: Supervised Learning Michael Littman, Charles Isbell, Pushkar Kolhe, GaTech ML-Udacity YouTube-Lectures 2015
16. ML: Unsupervised Learning Michael Littman, Charles Isbell, Pushkar Kolhe, GaTech ML-Udacity YouTube-Lectures 2015
17. Machine Learning Pedro Domingos, UWashington CSEP-546 YouTube-Lectures S2016
18. Statistical Machine Learning Larry Wasserman, CMU None YouTube-Lectures S2016
19. Machine Learning with Large Datasets William Cohen, CMU 10-605 YouTube-Lectures F2016
20. Statistical Learning - Classification Ali Ghodsi, University of Waterloo None YouTube-Lectures 2017
21. Machine Learning Andrew Ng, Stanford University Coursera-ML YouTube-Lectures 2017
22. Machine Learning Roni Rosenfield, CMU 10-601 YouTube-Lectures 2017
23. Statistical Machine Learning Ryan Tibshirani, Larry Wasserman, CMU 10-702 YouTube-Lectures S2017
24. Machine Learning for Computer Vision Fred Hamprecht, Heidelberg University None YouTube-Lectures F2017
25. Data Visualization Ali Ghodsi, University of Waterloo None YouTube-Lectures 2017
26. Machine Learning for Intelligent Systems Kilian Weinberger, Cornell University CS4780 YouTube-Lectures F2018
27. Statistical Learning Theory and Applications Tomaso Poggio, Lorenzo Rosasco, Sasha Rakhlin 9.520/6.860 YouTube-Lectures F2018
28. Machine Learning and Data Mining Mike Gelbart, University of British Columbia CPSC-340 YouTube-Lectures 2018
29. Foundations of Machine Learning David Rosenberg, Bloomberg FOML YouTube-Lectures 2018
30. Introduction to Machine Learning Andreas Krause, ETH Zuerich IntroML YouTube-Lectures 2018
31. Advanced Machine Learning Joachim Buhmann, ETH Zuerich AML-18 YouTube-Lectures 2018
32. Machine Learning Fundamentals Sanjoy Dasgupta, UC-San Diego MLF-slides YouTube-Lectures 2018
33. Machine Learning Jordan Boyd-Graber, University of Maryland CMSC-726 YouTube-Lectures 2015-2018
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Reinforcement Learning

S.No Course Name University/Instructor(s) Course Webpage Video Lectures Year
1. Short Course on Reinforcement Learning Satinder Singh, UMichigan None YouTube-Lectures 2011
2. Approximate Dynamic Programming Dimitri P. Bertsekas, MIT Lecture-Slides YouTube-Lectures 2014
3. Introduction to Reinforcement Learning David Silver, DeepMind UCL-RL YouTube-Lectures 2015
4. Reinforcement Learning Charles Isbell, Chris Pryby, GaTech; Michael Littman, Brown RL-Udacity YouTube-Lectures 2015
5. Reinforcement Learning Balaraman Ravindran, IIT Madras RL-IITM YouTube-Lectures 2016
6. Deep Reinforcement Learning Sergey Levine, UC Berkeley CS-294 YouTube-Lectures S2017
7. Deep Reinforcement Learning Sergey Levine, UC Berkeley CS-294 YouTube-Lectures F2017
8. Deep RL Bootcamp Many legends, UC Berkeley Deep-RL YouTube-Lectures 2017
9. Deep Reinforcement Learning Sergey Levine, UC Berkeley CS-294-112 YouTube-Lectures 2018
10. Reinforcement Learning Pascal Poupart, University of Waterloo CS-885 YouTube-Lectures 2018
11. Deep Reinforcement Learning and Control Katerina Fragkiadaki and Tom Mitchell, CMU 10-703 YouTube-Lectures 2018
12. Reinforcement Learning and Optimal Control Dimitri Bertsekas, Arizona State University RLOC Lecture-Videos 2019
13. Reinforcement Learning Emma Brunskill, Stanford University CS234 YouTube-Lectures 2019
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Probabilistic Graphical Models - *(Foundation for Graph Neural Networks)*

S.No Course Name University/Instructor(s) Course WebPage Lecture Videos Year
1. Probabilistic Graphical Models Many Legends, MPI-IS MLSS-Tuebingen YouTube-Lectures 2013
2. Probabilistic Modeling and Machine Learning Zoubin Ghahramani, University of Cambridge WUST-Wroclaw YouTube-Lectures 2013
3. Probabilistic Graphical Models Eric Xing, CMU 10-708 YouTube-Lectures 2014
4. Learning with Structured Data: An Introduction to Probabilistic Graphical Models Christoph Lampert, IST Austria None YouTube-Lectures 2016
5. Probabilistic Graphical Models Nicholas Zabaras, University of Notre Dame PGM YouTube-Lectures 2018
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Graph Neural Networks (Geometric DL)

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, Paris SGP-2018 YouTube-Lectures 2018
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Natural Language Processing - *(More Applied)*

S.No Course Name University/Instructor(s) Course WebPage Lecture Videos Year
1. Computational Linguistics I Jordan Boyd-Graber, University of Maryland CMS-723 YouTube-Lectures 2013-2018
2. Deep Learning for Natural Language Processing Nils Reimers, TU Darmstadt DL4NLP YouTube-Lectures 2015-2017
3. Deep Learning for Natural Language Processing Many Legends, DeepMind-Oxford DL-NLP YouTube-Lectures 2017
4. Deep Learning for Speech & Language UPC Barcelona DL-SL Lecture-Videos 2017
5. Neural Networks for Natural Language Processing Graham Neubig, CMU NN4NLP Code YouTube-Lectures 2017
6. Neural Networks for Natural Language Processing Graham Neubig, CMU NN4-NLP YouTube-Lectures 2018
7. Deep Learning for NLP Min-Yen Kan, NUS CS-6101 YouTube-Lectures 2018
8. Neural Networks for Natural Language Processing Graham Neubig, CMU NN4NLP YouTube-Lectures 2019
9. Natural Language Processing with Deep Learning Abigail See, Chris Manning, Richard Socher, Stanford University CS224n YouTube-Lectures 2019
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Automatic Speech Recognition

S.No Course Name University/Instructor(s) Course WebPage Lecture Videos Year
1. Deep Learning for Speech & Language UPC Barcelona DL-SL Lecture-Videos
YouTube-Videos
2017
2. Speech and Audio in the Northeast Many Legends, Google NYC SANE-15 YouTube-Videos 2015
3. Speech and Audio in the Northeast Many Legends, Google NYC SANE-17 YouTube-Videos 2017
4. Speech and Audio in the Northeast Many Legends, Google Cambridge SANE-18 YouTube-Videos 2018
-1. Deep Learning for Speech Recognition Many Legends, AoE None YouTube-Videos 2015-2018
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Modern Computer Vision

S.No Course Name University/Instructor(s) Course WebPage Lecture Videos Year
1. Computer Vision - (classical) Mubarak Shah, UCF CAP-5415 YouTube-Lectures 2012
2. Image and Video Processing: From Mars to Hollywood with a Stop at the Hospital Guillermo Sapiro, Duke university None YouTube-Videos 2013
3. Computer Vision - (classical) Mubarak Shah, UCF CAP-5415 YouTube-Lectures 2014
4. Multiple View Geometry (classical) Daniel Cremers, Technische Universität München mvg YouTube-Lectures 2013
5. Mathematical Methods for Robotics, Vision, and Graphics Justin Solomon, Stanford University CS-205A YouTube-Lectures 2013
6. Computer Vision for Visual Effects (classical) Rich Radke, Rensselaer Polytechnic Institute ECSE-6969 YouTube-Lectures S2014
7. Autonomous Navigation for Flying Robots Juergen Sturm, Technische Universität München Autonavx YouTube-Lectures 2014
8. SLAM - Mobile Robotics Cyrill Stachniss, Universitaet Freiburg RobotMapping YouTube-Lectures 2014
9. Computational Photography Irfan Essa, David Joyner, Arpan Chakraborty CP-Udacity YouTube-Lectures 2015
10. Introduction to Computer Vision (foundation) Aaron Bobick, Irfan Essa, Arpan Chakraborty CV-Udacity YouTube-Lectures 2016
11. Computer Vision Syed Afaq Ali Shah, University of Western Australia None YouTube-Lectures 2016
12. Deep Learning for Computer Vision UPC Barcelona DLCV-16
DLCV-17
DLCV-18
YouTube-Lectures 2016-2018
13. Convolutional Neural Networks Andrew Ng, Stanford University DeepLearning.AI YouTube-Lectures 2017
14. Variational Methods for Computer Vision Daniel Cremers, Technische Universität München VMCV YouTube-Lectures 2017
15. Winter School on Computer Vision Lots of Legends, Israel Institute for Advanced Studies WS-CV YouTube-Lectures 2017
16. Deep Learning for Visual Computing Debdoot Sheet, IIT-Kgp Nptel Notebooks YouTube-Lectures 2018
17. The Ancient Secrets of Computer Vision Joseph Redmon, Ali Farhadi TASCV ; TASCV-UW YouTube-Lectures 2018
18. Modern Robotics Kevin Lynch, Northwestern Robotics modern-robot YouTube-Lectures 2018
19. Digial Image Processing Alex Bronstein, Technion CS236860 YouTube-Lectures 2018
20. Mathematics of Imaging - Variational Methods and Optimization in Imaging Lots of Legends, Institut Henri Poincaré Workshop-1 YouTube-Lectures 2019
21. Deep Learning for Video Xavier Giró, UPC Barcelona deepvideo YouTube-Lectures 2019
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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. Machine Learning Summer School Lots of Legends, Reykjavik University MLSS-14 YouTube-Lectures 2014
5. Machine Learning Summer School Lots of Legends, Pittsburgh MLSS-14 YouTube-Lectures 2014
6. Deep Learning Summer School Lots of Legends, Université de Montréal DLSS-15 YouTube-Lectures 2015
7. Biomedical Image Analysis Summer School Lots of Legends, CentraleSupelec, Paris None YouTube-Lectures 2015
8. Mathematics of Signal Processing Lots of Legends, Hausdorff Institute for Mathematics SigProc YouTube-Lectures 2016
9. Microsoft Research - Machine Learning Course S V N Vishwanathan and Prateek Jain MS-Research None YouTube-Lectures 2016
10. Deep Learning Summer School Lots of Legends, Université de Montréal DL-SS-16 YouTube-Lectures 2016
11. Lisbon Machine Learning School Lots of Legends, Instituto Superior Técnico, Portugal LxMLS-16 YouTube-Lectures 2016
12. Machine Learning Advances and Applications Seminar Lots of Legends, Fields Institute, University of Toronto MLAAS-16 YouTube-Lectures
Video-Lectures
2016-2017
13. Machine Learning Advances and Applications Seminar Lots of Legends, Fields Institute, University of Toronto MLAAS-17 Video Lectures 2017-2018
14. Machine Learning Summer School Lots of Legends, MPI-IS Tübingen MLSS-17 YouTube-Lectures 2017
15. Representation Learning Lots of Legends, Simons Institute RepLearn YouTube-Lectures 2017
16. Foundations of Machine Learning Lots of Legends, Simons Institute ML-BootCamp YouTube-Lectures 2017
17. Optimization, Statistics, and Uncertainty Lots of Legends, Simons Institute Optim-Stats YouTube-Lectures 2017
18. Deep Learning: Theory, Algorithms, and Applications Lots of Legends, TU-Berlin DL: TAA YouTube-Lectures 2017
19. Deep Learning and Reinforcement Learning Summer School Lots of Legends, Université de Montréal DLRL-2017 Lecture-videos 2017
20. Statistical Physics Methods in Machine Learning Lots of Legends, International Centre for Theoretical Sciences, TIFR SPMML YouTube-Lectures 2017
21. Lisbon Machine Learning School Lots of Legends, Instituto Superior Técnico, Portugal LxMLS-17 YouTube-Lectures 2017
22. Foundations of Data Science Lots of Legends, Simons Institute DS-BootCamp YouTube-Lectures 2018
23. Deep Learning and Bayesian Methods Lots of Legends, HSE Moscow DLBM-SS YouTube-Lectures 2018
24. New Deep Learning Techniques Lots of Legends, IPAM UCLA IPAM-Workshop YouTube-Lectures 2018
25. Deep Learning and Reinforcement Learning Summer School Lots of Legends, University of Toronto DLRL-2018 Lecture-videos 2018
26. Machine Learning Summer School Lots of Legends, Universidad Autónoma de Madrid, Spain MLSS-18 YouTube-Lectures
Course-videos
2018
27. Theoretical Basis of Machine Learning Lots of Legends, International Centre for Theoretical Sciences, TIFR TBML-18 Lecture-Videos
YouTube-Videos
2018
28. Polish View on Machine Learning Lots of Legends, Warsaw PLinML-18 YouTube-Videos 2018
29. Machine Learning Advances and Applications Seminar Lots of Legends, Fields Institute, University of Toronto MLASS Video Lectures 2018-2019
30. MIFODS- ML, Stats, ToC seminar Lots of Legends, MIT MIFODS-seminar Lecture-videos 2018-2019
31. Learning Machines Seminar Series Lots of Legends, Cornell Tech LMSS YouTube-Lectures 2018-now
32. Machine Learning Summer School Lots of Legends, South Africa MLSS'19 YouTube-Lectures 2019
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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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