🎈 🎉 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.	Deep Learning Foundations and Applications	Debdoot Sheet and Sudeshna Sarkar, IIT-Kgp	AI61002	YouTube-Lectures	S2020
51.	Designing, Visualizing, and Understanding Deep Neural Networks	John Canny, UC Berkeley	CS 182/282A	YouTube-Lectures	S2020
52.	Deep Learning	Yann LeCun and Alfredo Canziani, NYU	DS-GA 1008	YouTube-Lectures	S2020
53.	Introduction to Deep Learning	Bhiksha Raj, CMU	11-785	YouTube-Lectures	S2020
54.	Deep Unsupervised Learning	Pieter Abbeel, UC Berkeley	CS294-158	YouTube-Lectures	S2020

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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.	Math Camp	Mark Walker, University of Arizona	UAMathCamp / Econ-519	YouTube-Lectures	2019

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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

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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.	Foundations of Machine Learning and Statistical Inference	Animashree Anandkumar, CalTech	CMS-165	YouTube-Lectures	2020

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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

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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

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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.	Deep Learning and Medical Applications	Lots of Legends, IPAM, UCLA	DLM-20	Lecture-Videos	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.	Geometry and Learning from Data in 3D and Beyond -Geometry and Learning from Data Tutorials	Lots of Legends, IPAM UCLA	GLDT	Lecture-Videos	2019
6.	Geometry and Learning from Data in 3D and Beyond - Geometric Processing	Lots of Legends, IPAM UCLA	GeoPro	Lecture-Videos	2019
7.	Geometry and Learning from Data in 3D and Beyond - Shape Analysis	Lots of Legends, IPAM UCLA	Shape-Analysis	Lecture-Videos	2019
8.	Geometry and Learning from Data in 3D and Beyond - Geometry of Big Data	Lots of Legends, IPAM UCLA	Geo-BData	Lecture-Videos	2019
9.	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

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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

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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	2019
32.	Sensors and State Estimation 2	Cyrill Stachniss, University of Bonn	None	YouTube-Lectures	S2020

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🌟 Boot Camps or Summer Schools 🍁

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S.No	Course Name	University/Instructor(s)	Course WebPage	Lecture Videos	Year
1.	Deep Learning, Feature Learning	Lots of Legends, IPAM UCLA	GSS-2012	YouTube-Lectures	2012
2.	Big Data Boot Camp	Lots of Legends, Simons Institute	Big Data	YouTube-Lectures	2013
3.	Machine Learning Summer School	Lots of Legends, MPI-IS Tübingen	MLSS-13	YouTube-Lectures	2013
4	Graduate Summer School: Computer Vision	Lots of Legends, IPAM-UCLA	GSS-CV	Video-Lectures	2013
5.	Machine Learning Summer School	Lots of Legends, Reykjavik University	MLSS-14	YouTube-Lectures	2014
6.	Machine Learning Summer School	Lots of Legends, Pittsburgh	MLSS-14	YouTube-Lectures	2014
7.	Deep Learning Summer School	Lots of Legends, Université de Montréal	DLSS-15	YouTube-Lectures	2015
8.	Biomedical Image Analysis Summer School	Lots of Legends, CentraleSupelec, Paris	None	YouTube-Lectures	2015
9.	Mathematics of Signal Processing	Lots of Legends, Hausdorff Institute for Mathematics	SigProc	YouTube-Lectures	2016
10.	Microsoft Research - Machine Learning Course	S V N Vishwanathan and Prateek Jain MS-Research	None	YouTube-Lectures	2016
11.	Deep Learning Summer School	Lots of Legends, Université de Montréal	DL-SS-16	YouTube-Lectures	2016
12.	Lisbon Machine Learning School	Lots of Legends, Instituto Superior Técnico, Portugal	LxMLS-16	YouTube-Lectures	2016
13.	Machine Learning Advances and Applications Seminar	Lots of Legends, Fields Institute, University of Toronto	MLAAS-16	YouTube-Lectures Video-Lectures	2016-2017
14.	Machine Learning Advances and Applications Seminar	Lots of Legends, Fields Institute, University of Toronto	MLAAS-17	Video Lectures	2017-2018
15.	Machine Learning Summer School	Lots of Legends, MPI-IS Tübingen	MLSS-17	YouTube-Lectures	2017
16.	Representation Learning	Lots of Legends, Simons Institute	RepLearn	YouTube-Lectures	2017
17.	Foundations of Machine Learning	Lots of Legends, Simons Institute	ML-BootCamp	YouTube-Lectures	2017
18.	Optimization, Statistics, and Uncertainty	Lots of Legends, Simons Institute	Optim-Stats	YouTube-Lectures	2017
19.	Deep Learning: Theory, Algorithms, and Applications	Lots of Legends, TU-Berlin	DL: TAA	YouTube-Lectures	2017
20.	Deep Learning and Reinforcement Learning Summer School	Lots of Legends, Université de Montréal	DLRL-2017	Lecture-videos	2017
21.	Statistical Physics Methods in Machine Learning	Lots of Legends, International Centre for Theoretical Sciences, TIFR	SPMML	YouTube-Lectures	2017
22.	Lisbon Machine Learning School	Lots of Legends, Instituto Superior Técnico, Portugal	LxMLS-17	YouTube-Lectures	2017
23.	Interactive Learning	Lots of Legends, Simons Institute, Berkeley	IL-2017	YouTube-Lectures	2017
24.	Computational Challenges in Machine Learning	Lots of Legends, Simons Institute, Berkeley	CCML-17	YouTube-Lectures	2017
25.	Foundations of Data Science	Lots of Legends, Simons Institute	DS-BootCamp	YouTube-Lectures	2018
26.	Deep Learning and Bayesian Methods	Lots of Legends, HSE Moscow	DLBM-SS	YouTube-Lectures	2018
27.	New Deep Learning Techniques	Lots of Legends, IPAM UCLA	IPAM-Workshop	YouTube-Lectures	2018
28.	Deep Learning and Reinforcement Learning Summer School	Lots of Legends, University of Toronto	DLRL-2018	Lecture-videos	2018
29.	Machine Learning Summer School	Lots of Legends, Universidad Autónoma de Madrid, Spain	MLSS-18	YouTube-Lectures Course-videos	2018
30.	Theoretical Basis of Machine Learning	Lots of Legends, International Centre for Theoretical Sciences, TIFR	TBML-18	Lecture-Videos YouTube-Videos	2018
31.	Polish View on Machine Learning	Lots of Legends, Warsaw	PLinML-18	YouTube-Videos	2018
32.	Big Data Analysis in Astronomy	Lots of Legends, Tenerife	BDAA-18	YouTube-Lectures	2018
33.	Machine Learning Advances and Applications Seminar	Lots of Legends, Fields Institute, University of Toronto	MLASS	Video Lectures	2018-2019
34.	MIFODS- ML, Stats, ToC seminar	Lots of Legends, MIT	MIFODS-seminar	Lecture-videos	2018-2019
35.	Learning Machines Seminar Series	Lots of Legends, Cornell Tech	LMSS	YouTube-Lectures	2018-now
36.	Machine Learning Summer School	Lots of Legends, South Africa	MLSS'19	YouTube-Lectures	2019
37.	Deep Learning Boot Camp	Lots of Legends, Simons Institute, Berkeley	DLBC-19	YouTube-Lectures	2019
38.	Frontiers of Deep Learning	Lots of Legends, Simons Institute, Berkeley	FoDL-19	YouTube-Lectures	2019
39.	Mathematics of data: Structured representations for sensing, approximation and learning	Lots of Legends, The Alan Turing Institute, London	MoD-19	YouTube-Lectures	2019
40.	Deep Learning and Bayesian Methods	Lots of Legends, HSE Moscow	DLBM-SS	YouTube-Lectures	2019
41.	The Mathematics of Deep Learning and Data Science	Lots of Legends, Isaac Newton Institute, Cambridge	MoDL-DS	Lecture-Videos	2019
42.	Geometry of Deep Learning	Lots of Legends, MSR Redmond	GoDL	YouTube-Lectures	2019
43.	Deep Learning for Science School	Many folks, LBNL, Berkeley	DLfSS	YouTube-Lectures	2019
44.	Emerging Challenges in Deep Learning	Lots of Legends, Simons Institute, Berkeley	ECDL	YouTube-Lectures	2019
45.	Full Stack Deep Learning	Pieter Abbeel and many others, UC Berkeley	FSDL-M19	YouTube-Lectures-Day-1 Day-2	2019
46.	Algorithmic and Theoretical aspects of Machine Learning	Lots of legends, IIIT-Bengaluru	ACM-ML nptel	YouTube-Lectures	2019
47.	Deep Learning and Reinforcement Learning Summer School	Lots of Legends, AMII, Edmonton, Canada	DLRL-2019	YouTube-Lectures	2019
48.	Mathematics of Machine Learning - Summer Graduate School	Lots of Legends, University of Washington	MoML-SGS, MoML-SS	YouTube-Lectures	2019
49.	Workshop on Theory of Deep Learning: Where next?	Lots of Legends, IAS, Princeton University	WTDL	YouTube-Lectures	2019
50.	Computational Vision Summer School	Lots of Legends, Black Forest, Germany	CVSS-2019	YouTube-Lectures	2019
51.	Learning under complex structure	Lots of Legends, MIT	LUCS	YouTube-Lectures	2020

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🐦 Bird's Eye view of A(G)I 🦅

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S.No	Course Name	University/Instructor(s)	Course WebPage	Lecture Videos	Year
1.	Artificial General Intelligence	Lots of Legends, MIT	6.S099-AGI	Lecture-Videos	2018-2019
2.	AI Podcast	Lots of Legends, MIT	AI-Pod	YouTube-Lectures	2018-2019
3.	NYU - AI Seminars	Lots of Legends, NYU	modern-AI	YouTube-Lectures	2017-now
4.	Deep Learning: Alchemy or Science?	Lots of Legends, Institute for Advanced Study, Princeton	DLAS Agenda	YouTube-Lectures	2019

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To-Do 🏃

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

⬜ Computer Vision courses which are DL & ML heavy

⬜ Speech recognition courses which are DL heavy

⬜ Structured Courses on Geometric, Graph Neural Networks

⬜ Section on Autonomous Vehicles

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Go to Contents ⤴️

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Around the Web 🌏

• Montreal.AI
• UPC-DLAI-2018
• UPC-DLAI-2019
• www.hashtagtechgeek.com
• UPC-Barcelona, IDL-2020

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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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