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🎉 Deep Learning Drizzle 🎊 🎈

Contents

  • Deep Learning (Deep Neural Networks) ⤵️

  • Machine Learning Fundamentals ⤵️

  • Optimization for Machine Learning ⤵️

  • General Machine Learning ⤵️

  • Reinforcement Learning ⤵️

  • Probabilistic Graphical Models ⤵️

  • Natural Language Processing ⤵️

  • Modern Computer Vision ⤵️

  • Boot Camps or Summer Schools ⤵️

🎉 Deep Learning 🎊 🎈

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 mirror 2012 2014
2. Neural Networks Demystified Stephen Welch, Welch Labs Supplementary Code YouTube-Lectures 2014
3. Deep Learning at Oxford Nando de Freitas, Oxford University Oxford-ML YouTube-Lectures 2015
4. CS231n: CNNs for Visual Recognition Andrej Karpathy, Stanford University CS231n None 2015
5. CS231n: CNNs for Visual Recognition Andrej Karpathy, Stanford University CS231n YouTube-Lectures 2016
6. CS231n: CNNs for Visual Recognition Justin Johnson, Stanford University CS231n YouTube-Lectures 2017
7. CS224d: Deep Learning for NLP Richard Socher, Stanford University CS224d YouTube-Lectures 2015
8. CS224d: Deep Learning for NLP Richard Socher, Stanford University CS224d YouTube-Lectures 2016
9. CS224n: NLP with Deep Learning Richard Socher, Stanford University CS224n YouTube-Lectures 2017
10. Neural Networks Hugo Larochelle, Université de Sherbrooke Neural-Networks YouTube-Lectures 2016
11. Deep Learning Andrew Ng, Stanford University CS230 None 2018
12. Bay Area Deep Learning Many legends, Stanford None YouTube-Lectures 2016
13. UvA Deep Learning Efstratios Gavves, University of Amsterdam(UvA) UvA-DLC Lecture-Videos 2018
14. Advanced Deep Learning and Reinforcement Learning Many legends, DeepMind None YouTube-Lectures 2018
15. Deep Learning Francois Fleuret, EPFL EE-59 None 2019
16. Deep Learning Francois Fleuret, EPFL EE-59 Video-Lectures 2018
17. Deep Learning for Perception Dhruv Batra, Virginia Tech ECE-6504 YouTube-Lectures 2015
18. Introduction to Deep Learning Alexander Amini, Harini Suresh, MIT 6.S191 YouTube-Lectures 2018
19. Deep Learning for Self-Driving Cars Lex Fridman, MIT 6.S094 YouTube-Lectures 2017-2018
20. MIT Deep Learning Many Researchers, Lex Fridman, MIT 6.S094, 6.S091, 6.S093 YouTube-Lectures 2019
21. Introduction to Deep Learning Biksha Raj and many others, CMU 11-485/785 YouTube-Lectures S2018
22. Introduction to Deep Learning Biksha Raj and others, CMU 11-485/785 YouTube-Lectures Recitation-Inclusive F2018
23. Deep Learning Specialization Andrew Ng, Stanford DeepLearning.AI YouTube-Lectures 2017-2018
24. Deep Learning Ali Ghodsi, University of Waterloo STAT-946 YouTube-Lectures F2015
25. Deep Learning Ali Ghodsi, University of Waterloo STAT-946 YouTube-Lectures F2017
26. Deep Learning Mitesh Khapra, IIT-Madras CS7015 YouTube-Lectures 2018
-2. Deep Learning Book companion videos Ian Goodfellow and others DL-book slides YouTube-Lectures 2017
-1. Neural Networks Grant Sanderson None YouTube-Lectures 2017-2018

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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. Linear Algebra: An in-depth Introduction Pavel Grinfeld None Part-1
Part-2
Part-3
Part-4		 2015- 2017
3. Essence of Linear Algebra Grant Sanderson None YouTube-Lectures 2016
4. Essence of Calculus Grant Sanderson None YouTube-Lectures 2017-2018
5. Mathematics for Machine Learning (Linear Algebra, Calculus) David Dye, Samuel Cooper, and Freddie Page, IC-London MML YouTube-Lectures 2018
6. Machine Learning Fundamentals Sanjoy Dasgupta, UC-San Diego MLF-slides YouTube-Lectures 2018

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💘 Optimization for Machine Learning 🌀 💥

S.No Course Name University/Instructor(s) Course Webpage Video Lectures Year
1. Optimization Geoff Gordon & Ryan Tibshirani, CMU 10-725 YouTube-Lectures 2012
2. Convex Optimization Ryan Tibshirani, CMU cvx-opt YouTube-Lectures F2018
3. Convex Optimization Stephen Boyd, Stanford University ee364a YouTube-Lectures 2008

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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 and Data Mining Nando de Freitas, University of British Columbia CPSC-340 YouTube-Lectures 2012
3. Learning from Data Yaser Abu-Mostafa, CalTech CS156 YouTube-Lectures 2012
4. Machine Learning Rudolph Triebel, TUM Machine Learning YouTube-Lectures 2013
5. Introduction to Machine Learning Katie Malone, Sebastian Thrun, Udacity ML-Udacity YouTube-Lectures 2015
6. Introduction to Machine Learning Dhruv Batra, Virginia Tech ECE-5984 YouTube-Lectures 2015
7. Statistical Learning - Classification Ali Ghodsi, University of Waterloo STAT-441 YouTube-Lectures 2015
8 Machine Learning Theory Shai Ben-David, University of Waterloo None YouTube-Lectures 2015
9. Introduction to Machine Learning Alex Smola, CMU 10-701 YouTube-Lectures S2015
10. ML: Supervised Learning Michael Littman, Charles Isbell, Pushkar Kolhe, GaTech ML-Udacity YouTube-Lectures 2015
11. ML: Unsupervised Learning Michael Littman, Charles Isbell, Pushkar Kolhe, GaTech ML-Udacity YouTube-Lectures 2015
12. Statistical Machine Learning Larry Wasserman, CMU None YouTube-Lectures S2016
13. Statistical Learning - Classification Ali Ghodsi, University of Waterloo None YouTube-Lectures 2017
14. Machine Learning Andrew Ng, Stanford University Coursera-ML YouTube-Lectures 2017
15. Statistical Machine Learning Ryan Tibshirani, Larry Wasserman, CMU 10-702 YouTube-Lectures S2017
16. Machine Learning for Intelligent Systems Kilian Weinberger, Cornell University CS4780 YouTube-Lectures F2018
17. Statistical Learning Theory and Applications Tomaso Poggio, Lorenzo Rosasco, Sasha Rakhlin 9.520/6.860 YouTube-Lectures F2018
18. Machine Learning and Data Mining Mike Gelbart, University of British Columbia CPSC-340 YouTube-Lectures 2018
19. Foundations of Machine Learning David Rosenberg, Bloomberg FOML YouTube-Lectures 2018

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🎈 Reinforcement Learning ♨️ 🎮

S.No Course Name University/Instructor(s) Course Webpage Video Lectures Year
1. Approximate Dynamic Programming Dimitri P. Bertsekas, MIT Lecture-Slides YouTube-Lectures 2014
2. Introduction to Reinforcement Learning David Silver, DeepMind UCL-RL YouTube-Lectures 2015
3. Reinforcement Learning Charles Isbell, Chris Pryby, GaTech; Michael Littman, Brown RL-Udacity YouTube-Lectures 2015
4. Reinforcement Learning Balaraman Ravindran, IIT Madras RL-IITM YouTube-Lectures 2016
5. Deep Reinforcement Learning Sergey Levine, UC Berkeley CS-294 YouTube-Lectures S2017
6. Deep Reinforcement Learning Sergey Levine, UC Berkeley CS-294 YouTube-Lectures F2017
7. Deep RL Bootcamp Many legends, UC Berkeley Deep-RL YouTube-Lectures 2017
8. Deep Reinforcement Learning Sergey Levine, UC Berkeley CS-294-112 YouTube-Lectures 2018
9. Reinforcement Learning Pascal Poupart, University of Waterloo CS-885 YouTube-Lectures 2018
10. Deep Reinforcement Learning and Control Katerina Fragkiadaki and Tom Mitchell, CMU 10-703 YouTube-Lectures 2018

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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. Probabilistic Graphical Models Nicholas Zabaras, University of Notre Dame PGM 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. Deep Learning for Natural Language Processing Nils Reimers, TU Darmstadt DL4NLP YouTube-Lectures 2015-2017
2. Deep Learning for Natural Language Processing Many Legends, DeepMind-Oxford DL-NLP YouTube-Lectures 2017
3. Neural Networks for Natural Language Processing Graham Neubig, CMU NN4NLP Code YouTube-Lectures 2017
4. Neural Networks for Natural Language Processing Graham Neubig, CMU NN4-NLP YouTube-Lectures 2018
5. Neural Networks for Natural Language Processing Graham Neubig, CMU NN4NLP YouTube-Lectures 2019

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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. Computer Vision - (classical) Mubarak Shah, UCF CAP-5415 YouTube-Lectures 2014
3. Introduction to Computer Vision (foundation) Aaron Bobick, Irfan Essa, Arpan Chakraborty CV-Udacity YouTube-Lectures 2016
4. Convolutional Neural Networks Andrew Ng, Stanford University DeepLearning.AI YouTube-Lectures 2017
5. Variational Methods for Computer Vision Daniel Cremers, TUM VMCV YouTube-Lectures 2017
6. Deep Learning for Visual Computing Debdoot Sheet, IIT-Kgp Nptel Notebooks YouTube-Lectures 2018
7. Autonomous Navigation for Flying Robots Juergen Sturm, TUM Autonavx YouTube-Lectures 2014
8. SLAM - Mobile Robotics Cyrill Stachniss, Universitaet Freiburg RobotMapping YouTube-Lectures 2014

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🌟 Boot Camps or Summer Schools 🍁

S.No Course Name University/Instructor(s) Course WebPage Lecture Videos Year
1. Optimization for Machine Learning S V N Vishwanathan, Purdue University None YouTube-Lectures 2011
2. Deep Learning, Feature Learning Many legends, IPAM UCLA GSS-2012 YouTube-Lectures 2012
3. Big Data Boot Camp Many Legends, Simons Institute Big Data YouTube-Lectures 2013
4 Mathematics of Signal Processing Many Legends, Hausdorff Institute for Mathematics SigProc YouTube-Lectures 2016
5. Microsoft Research - Machine Learning Course S V N Vishwanathan and Prateek Jain MS-Research None YouTube-Lectures 2016
6. Deep Learning Summer School Many Legends, Université de Montréal DL-SS-16 YouTube-Lectures 2016
7. Representation Learning Many Legends, Simons Institute RepLearn YouTube-Lectures 2017
8. Foundations of Machine Learning Many Legends, Simons Institute ML-BootCamp YouTube-Lectures 2017
9. Optimization, Statistics, and Uncertainty Many Legends, Simons Institute Optim-Stats YouTube-Lectures 2017
10. Deep Learning: Theory, Algorithms, and Applications Many Legends, TU-Berlin DL: TAA YouTube-Lectures 2017
11. Foundations of Data Science Many Legends, Simons Institute DS-BootCamp YouTube-Lectures 2018
12. Deep|Bayes Many Legends, HSE Moscow DeepBayes.ru YouTube-Lectures 2018
13. New Deep Learning Techniques Many Legends, IPAM UCLA IPAM-Workshop YouTube-Lectures 2018

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To-Do 🏃

⬜ Optimization courses which form the foundation for ML, DL, RL

⬜ Computer Vision courses which are DL & ML heavy

⬜ NLP courses which are DL, RL, & ML heavy

⬜ Speech recognition courses which are DL heavy

⬜ Courses on Graph Neural Networks

⬜ Section on DL/RL/ML Summer School Lectures

Go to Contents ⤴️

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 - optional), then please raise an issue or send a PR by updating the course according to the above format.

Danke Sehr!

💝 🎓 🎓 🎓 🎓 🎓 🎓 🎓 🎓 🎓🎓 💝