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

:balloon: :tada: Deep Learning Drizzle :confetti_ball: :balloon:

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

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Deep Learning (Deep Neural Networks) :arrow_heading_down:	Probabilistic Graphical Models :arrow_heading_down:

Machine Learning Fundamentals :arrow_heading_down:	Natural Language Processing :arrow_heading_down:

Optimization for Machine Learning :arrow_heading_down:	Automatic Speech Recognition :arrow_heading_down:

General Machine Learning :arrow_heading_down:	Modern Computer Vision :arrow_heading_down:

Reinforcement Learning :arrow_heading_down:	Boot Camps or Summer Schools :arrow_heading_down:

Bayesian Deep Learning :arrow_heading_down:	Medical Imaging :arrow_heading_down:

Graph Neural Networks :arrow_heading_down:	Bird's-eye view of Artificial Intelligence :arrow_heading_down:

:tada: Deep Learning (Deep Neural Networks) :confetti_ball: :balloon:

S.No	Course Name	University/Instructor(s)	Course WebPage	Lecture Videos	Year
1.	Neural Networks for Machine Learning	Geoffrey Hinton, University of Toronto	Lecture-Slides
CSC321-tijmen	YouTube-Lectures
UofT-mirror	2012
2014
2.	Neural Networks Demystified	Stephen Welch, Welch Labs	Suppl. Code	YouTube-Lectures	2014
3.	Deep Learning at Oxford	Nando de Freitas, Oxford University	Oxford-ML	YouTube-Lectures	2015
4.	Deep Learning for Perception	Dhruv Batra, Virginia Tech	ECE-6504	YouTube-Lectures	2015
5.	Deep Learning	Ali Ghodsi, University of Waterloo	STAT-946	YouTube-Lectures	F2015
6.	CS231n: CNNs for Visual Recognition	Andrej Karpathy, Stanford University	CS231n	`None`	2015
7.	CS224d: Deep Learning for NLP	Richard Socher, Stanford University	CS224d	YouTube-Lectures	2015
8.	Bay Area Deep Learning	Many legends, Stanford	`None`	YouTube-Lectures	2016
9.	CS231n: CNNs for Visual Recognition	Andrej Karpathy, Stanford University	CS231n	YouTube-Lectures
(Academic Torrent)	2016
10.	Neural Networks	Hugo Larochelle, Université de Sherbrooke	Neural-Networks	YouTube-Lectures
(Academic Torrent)	2016

11.	CS224d: Deep Learning for NLP	Richard Socher, Stanford University	CS224d	YouTube-Lectures
(Academic Torrent)	2016
12.	CS224n: NLP with Deep Learning	Richard Socher, Stanford University	CS224n	YouTube-Lectures	2017
13.	CS231n: CNNs for Visual Recognition	Justin Johnson, Stanford University	CS231n	YouTube-Lectures
(Academic Torrent)	2017
14.	Topics in Deep Learning	Ruslan Salakhutdinov, CMU	10707	YouTube-Lectures	F2017
15.	Deep Learning Crash Course	Leo Isikdogan, UT Austin	`None`	YouTube-Lectures	2017
16.	Deep Learning and its Applications	François Pitié, Trinity College Dublin	EE4C16	YouTube-Lectures	2017
17.	Deep Learning	Andrew Ng, Stanford University	CS230	YouTube-Lectures	2018
18.	UvA Deep Learning	Efstratios Gavves, University of Amsterdam	UvA-DLC	Lecture-Videos	2018
19.	Advanced Deep Learning and Reinforcement Learning	Many legends, DeepMind	`None`	YouTube-Lectures	2018
20.	Machine Learning	Peter Bloem, Vrije Universiteit Amsterdam	MLVU	YouTube-Lectures	2018

21.	Deep Learning	Francois Fleuret, EPFL	EE-59	Video-Lectures	2018
22.	Introduction to Deep Learning	Alexander Amini, Harini Suresh and others, MIT	6.S191	YouTube-Lectures
2017-version	2017- 2021
23.	Deep Learning for Self-Driving Cars	Lex Fridman, MIT	6.S094	YouTube-Lectures	2017-2018
24.	Introduction to Deep Learning	Bhiksha Raj and many others, CMU	11-485/785	YouTube-Lectures	S2018
25.	Introduction to Deep Learning	Bhiksha Raj and many others, CMU	11-485/785	YouTube-Lectures Recitation-Inclusive	F2018
26.	Deep Learning Specialization	Andrew Ng, Stanford	DL.AI	YouTube-Lectures	2017-2018
27.	Deep Learning	Ali Ghodsi, University of Waterloo	STAT-946	YouTube-Lectures	F2017
28.	Deep Learning	Mitesh Khapra, IIT-Madras	CS7015	YouTube-Lectures	2018
29.	Deep Learning for AI	UPC Barcelona	DLAI-2017
DLAI-2018	YouTube-Lectures	2017-2018
30.	Deep Learning	Alex Bronstein and Avi Mendelson, Technion	CS236605	YouTube-Lectures	2018

31.	MIT Deep Learning	Many Researchers, Lex Fridman, MIT	6.S094, 6.S091, 6.S093	YouTube-Lectures	2019
32.	Deep Learning Book companion videos	Ian Goodfellow and others	DL-book slides	YouTube-Lectures	2017
33.	Theories of Deep Learning	Many Legends, Stanford	Stats-385	YouTube-Lectures
(first 10 lectures)	F2017
34.	Neural Networks	Grant Sanderson	`None`	YouTube-Lectures	2017-2018
35.	CS230: Deep Learning	Andrew Ng, Kian Katanforoosh, Stanford	CS230	YouTube-Lectures	A2018
36.	Theory of Deep Learning	Lots of Legends, Canary Islands	DALI'18	YouTube-Lectures	2018
37.	Introduction to Deep Learning	Alex Smola, UC Berkeley	Stat-157	YouTube-Lectures	S2019
38.	Deep Unsupervised Learning	Pieter Abbeel, UC Berkeley	CS294-158	YouTube-Lectures	S2019
39.	Machine Learning	Peter Bloem, Vrije Universiteit Amsterdam	MLVU	YouTube-Lectures	2019
40.	Deep Learning on Computational Accelerators	Alex Bronstein and Avi Mendelson, Technion	CS236605	YouTube-Lectures	S2019

41.	Introduction to Deep Learning	Bhiksha Raj and many others, CMU	11-785	YouTube-Lectures	S2019
42.	Introduction to Deep Learning	Bhiksha Raj and many others, CMU	11-785	YouTube-Lectures
Recitations	F2019
43.	UvA Deep Learning	Efstratios Gavves, University of Amsterdam	UvA-DLC	Lecture-Videos	S2019
44.	Deep Learning	Prabir Kumar Biswas, IIT Kgp	`None`	YouTube-Lectures	2019
45.	Deep Learning and its Applications	Aditya Nigam, IIT Mandi	CS-671	YouTube-Lectures	2019
46.	Neural Networks	Neil Rhodes, Harvey Mudd College	CS-152	YouTube-Lectures	F2019
47.	Deep Learning	Thomas Hofmann, ETH Zürich	DAL-DL	Lecture-Videos	F2019
48.	Deep Learning	Milan Straka, Charles University	NPFL114	Lecture-Videos	S2019
49.	UvA Deep Learning	Efstratios Gavves, University of Amsterdam	UvA-DLC-19	Lecture-Videos	F2019
50.	Artificial Intelligence: Principles and Techniques	Percy Liang and Dorsa Sadigh, Stanford University	CS221	YouTube-Lectures	F2019

51.	Analyses of Deep Learning	Lots of Legends, Stanford University	STATS-385	YouTube-Lectures	2017-2019
52.	Deep Learning Foundations and Applications	Debdoot Sheet and Sudeshna Sarkar, IIT-Kgp	AI61002	YouTube-Lectures	S2020
53.	Designing, Visualizing, and Understanding Deep Neural Networks	John Canny, UC Berkeley	CS 182/282A	YouTube-Lectures	S2020
54.	Deep Learning	Yann LeCun and Alfredo Canziani, NYU	DS-GA 1008	YouTube-Lectures	S2020
55.	Introduction to Deep Learning	Bhiksha Raj, CMU	11-785	YouTube-Lectures	S2020
56.	Deep Unsupervised Learning	Pieter Abbeel, UC Berkeley	CS294-158	YouTube-Lectures	S2020
57.	Machine Learning	Peter Bloem, Vrije Universiteit Amsterdam	VUML	YouTube-Lectures	S2020
58.	Deep Learning (with PyTorch)	Alfredo Canziani and Yann LeCun, NYU	DS-GA 1008	YouTube-Lectures	S2020
59.	Introduction to Deep Learning and Generative Models	Sebastian Raschka, UW-Madison	Stat453	YouTube-Lectures	S2020
60.	Deep Learning	Andreas Maier, FAU Erlangen-Nürnberg	DL-2020	YouTube-Lectures
Lecture-Videos	SS2020

61.	Introduction to Deep Learning	Laura Leal-Taixé and Matthias Niessner, TU-München	I2DL-IN2346	YouTube-Lectures	SS2020
62.	Deep Learning	Sargur Srihari, SUNY-Buffalo	CSE676	YouTube-Lectures-P1
YouTube-Lectures-P2	2020
63.	Deep Learning Lecture Series	Lots of Legends, DeepMind x UCL, London	DLLS-20	YouTube-Lectures	2020
64.	MultiModal Machine Learning	Louis-Philippe Morency & others, Carnegie Mellon University	11-777 MMML-20	YouTube-Lectures	F2020
65.	Reliable and Interpretable Artificial Intelligence	Martin Vechev, ETH Zürich	RIAI-20	YouTube-Lectures	F2020
66.	Fundamentals of Deep Learning	David McAllester, Toyota Technological Institute, Chicago	TTIC-31230	YouTube-Lectures	F2020
67.	Foundations of Deep Learning	Soheil Feize, University of Maryland, College Park	CMSC 828W	YouTube-Lectures	F2020
68.	Deep Learning	Andreas Geiger, Universität Tübingen	DL-UT	YouTube-Lectures	W20/21
69.	Deep Learning	Andreas Maier, FAU Erlangen-Nürnberg	DL-FAU	YouTube-Lectures	W20/21
70.	Fundamentals of Deep Learning	Terence Parr and Yannet Interian, University of San Francisco	DL-Fundamentals	YouTube-Lectures	S2021

71.	Full Stack Deep Learning	Pieter Abbeel, Sergey Karayev, UC Berkeley	FS-DL	YouTube-Lectures	S2021
72.	Deep Learning: Designing, Visualizing, and Understanding DNNs	Sergey Levine, UC Berkeley	CS 182	YouTube-Lectures	S2021
73.	Deep Learning in the Life Sciences	Manolis Kellis, MIT	6.874	YouTube-Lectures	S2021
74.	Introduction to Deep Learning and Generative Models	Sebastian Raschka, University of Wisconsin-Madison	Stat 453	YouTube-Lectures	S2021
75.	Deep Learning	Alfredo Canziani and Yann LeCun, NYU	NYU-DLSP21	YouTube-Lectures	S2021
76.	Applied Deep Learning	Alexander Pacha, TU Wien	`None`	YouTube-Lectures	2020-2021
77.	Machine Learning	Hung-yi Lee, National Taiwan University	ML'21	YouTube-Lectures	S2021
78.	Mathematics of Deep Learning	Lots of legends, FAU	MoDL	Lecture-Videos	2019-21
79.	Deep Learning	Peter Bloem, Michael Cochez, and Jakub Tomczak, VU-Amsterdam	DL	YouTube-Lectures	2020-21
80.	Applied Deep Learning	Maziar Raissi, UC Boulder	ADL'21	YouTube-Lectures	2021

81.	An Introduction to Group Equivariant Deep Learning	Erik J. Bekkers, Universiteit van Amsterdam	UvAGEDL	YouTube-Lectures	2022

Go to Contents :arrow_heading_up:

:cupid: Machine Learning Fundamentals :cyclone: :boom:

S.No	Course Name	University/Instructor(s)	Course Webpage	Video Lectures	Year
1.	Linear Algebra	Gilbert Strang, MIT	18.06 SC	YouTube-Lectures	2011
2.	Probability Primer	Jeffrey Miller, Brown University	`mathematical monk`	YouTube-Lectures	2011
3.	Information Theory, Pattern Recognition, and Neural Networks	David Mackay, University of Cambridge	ITPRNN	YouTube-Lectures	2012
4.	Linear Algebra Review	Zico Kolter, CMU	LinAlg	YouTube-Lectures	2013
5.	Probability and Statistics	Michel van Biezen	`None`	YouTube-Lectures	2015
6.	Linear Algebra: An in-depth Introduction	Pavel Grinfeld	`None`	Part-1
Part-2
Part-3
Part-4	2015- 2017
7.	Multivariable Calculus	Grant Sanderson, Khan Academy	`None`	YouTube-Lectures	2016
8.	Essence of Linear Algebra	Grant Sanderson	`None`	YouTube-Lectures	2016
9.	Essence of Calculus	Grant Sanderson	`None`	YouTube-Lectures	2017-2018
10.	Math Background for Machine Learning	Geoff Gordon, CMU	10-606, 10-607	YouTube-Lectures	F2017

11.	Mathematics for Machine Learning (Linear Algebra, Calculus)	David Dye, Samuel Cooper, and Freddie Page, IC-London	MML	YouTube-Lectures	2018
12.	Multivariable Calculus	S.K. Gupta and Sanjeev Kumar, IIT-Roorkee	MVC	YouTube-Lectures	2018
13.	Engineering Probability	Rich Radke, Rensselaer Polytechnic Institute	`None`	YouTube-Lectures	2018
14.	Matrix Methods in Data Analysis, Signal Processing, and Machine Learning	Gilbert Strang, MIT	18.065	YouTube-Lectures	S2018
15.	Information Theory	Himanshu Tyagi, IISC, Bengaluru	E2 201	YouTube-Lectures	2018-20
16.	Math Camp	Mark Walker, University of Arizona	UAMathCamp / Econ-519	YouTube-Lectures	2019
17.	A 2020 Vision of Linear Algebra	Gilbert Strang, MIT	VoLA	YouTube-Lectures	S2020
18.	Mathematics for Numerical Computing and Machine Learning	Szymon Rusinkiewicz, Princeton University	COS-302	YouTube-Lectures	F2020
19.	Essential Statistics for Neuroscientists	Philipp Berens, Universität Klinikum Tübingen	`None`	YouTube-Lectures	2020
20.	Mathematics for Machine Learning	Ulrike von Luxburg, Eberhard Karls Universität Tübingen	Math4ML	YouTube-Lectures	W2020
21.	Introduction to Causal Inference	Brady Neal, Mila, Montréal	CausalInf	YouTube-Lectures	F2020
22.	Applied Linear Algebra	Andrew Thangaraj, IIT Madras	EE5120	YouTube-Lectures	2021
23.	Mathematical Tools for Data Science	Carlos Fernandez-Granda, New York University	DS-GA 1013/Math-GA 2824	YouTube-Lectures	2021
24.	Mathematics for Numerical Computing and Machine Learning	Ryan Adams, Princeton University	COS 302 / SML 305	YouTube-Lectures	2021

Go to Contents :arrow_heading_up:

:cupid: Optimization for Machine Learning :cyclone: :boom:

S.No	Course Name	University/Instructor(s)	Course Webpage	Video Lectures	Year
1.	Convex Optimization	Stephen Boyd, Stanford University	ee364a	YouTube-Lectures	2008
2.	Introduction to Optimization	Michael Zibulevsky, Technion	CS-236330	YouTube-Lectures	2009
3.	Optimization for Machine Learning	S V N Vishwanathan, Purdue University	`None`	YouTube-Lectures	2011
4.	Optimization	Geoff Gordon & Ryan Tibshirani, CMU	10-725	YouTube-Lectures	2012
5.	Convex Optimization	Joydeep Dutta, IIT-Kanpur	cvx-nptel	YouTube-Lectures	2013
6.	Foundations of Optimization	Joydeep Dutta, IIT-Kanpur	fop-nptel	YouTube-Lectures	2014
7.	Algorithmic Aspects of Machine Learning	Ankur Moitra, MIT	18.409-AAML	YouTube-Lectures	S2015
8.	Numerical Optimization	Shirish K. Shevade, IISC	`None`	YouTube-Lectures	2015
9.	Convex Optimization	Ryan Tibshirani, CMU	10-725	YouTube-Lectures	S2015
10.	Convex Optimization	Ryan Tibshirani, CMU	10-725	YouTube-Lectures	F2015
11.	Advanced Algorithms	Ankur Moitra, MIT	6.854-AA	YouTube-Lectures	S2016
12.	Introduction to Optimization	Michael Zibulevsky, Technion	`None`	YouTube-Lectures	2016
13.	Convex Optimization	Javier Peña & Ryan Tibshirani	10-725/36-725	YouTube-Lectures	F2016
14.	Convex Optimization	Ryan Tibshirani, CMU	10-725	YouTube-Lectures
Lecture-Videos	F2018
15.	Modern Algorithmic Optimization	Yurii Nesterov, UCLouvain	`None`	YouTube-Lectures	2018
16.	Optimization, Foundations of Optimization	Mark Walker, University of Arizona	MathCamp-20	YouTube-Lectures-Found.
YouTube-Lectures-Opt	2019 - now
17.	Optimization: Principles and Algorithms	Michel Bierlaire, École polytechnique fédérale de Lausanne (EPFL)	opt-algo	YouTube-Lectures	2019
18.	Optimization and Simulation	Michel Bierlaire, École polytechnique fédérale de Lausanne (EPFL)	opt-sim	YouTube-Lectures	S2019
19.	Brazilian Workshop on Continuous Optimization	Lots of Legends, Instituto Nacional de Matemática Pura e Aplicada, Rio de Janeiro	cont. opt.	YouTube-Lectures	2019
20.	One World Optimization Seminar	Lots of Legends, Universität Wien	1W-OPT	YouTube-Lectures	2020-

21.	Convex Optimization II	Constantine Caramanis, UT Austin	CVX-Optim-II	YouTube-Lectures	S2020
22.	Combinatorial Optimization	Constantine Caramanis, UT Austin	comb-op	YouTube-Lectures	F2020
23.	Optimization Methods for Machine Learning and Engineering	Julius Pfrommer, Jürgen Beyerer, Karlsruher Institut für Technologie (KIT)	Optim-MLE, slides	YouTube-Lectures	W2020-21

Go to Contents :arrow_heading_up:

:cupid: General Machine Learning :cyclone: :boom:

S.No	Course Name	University/Instructor(s)	Course Webpage	Video Lectures	Year
1.	CS229: Machine Learning	Andrew Ng, Stanford University	CS229-old
CS229-new	YouTube-Lectures	2007
2.	Machine Learning	Jeffrey Miller, Brown University	`mathematical monk`	YouTube-Lectures	2011
3.	Machine Learning	Tom Mitchell, CMU	10-701	Lecture-Videos	2011
4.	Machine Learning and Data Mining	Nando de Freitas, University of British Columbia	CPSC-340	YouTube-Lectures	2012
5.	Learning from Data	Yaser Abu-Mostafa, CalTech	CS156	YouTube-Lectures	2012
6.	Machine Learning	Rudolph Triebel, Technische Universität München	Machine Learning	YouTube-Lectures	2013
7.	Introduction to Machine Learning	Alex Smola, CMU	10-701	YouTube-Lectures	2013
8.	Introduction to Machine Learning	Alex Smola and Geoffrey Gordon, CMU	10-701x	YouTube-Lectures	2013
9.	Pattern Recognition	Sukhendu Das, IIT-M and C.A. Murthy, ISI-Calcutta	PR-NPTEL	YouTube-Lectures	2014
10.	An Introduction to Statistical Learning with Applications in R	Trevor Hastie and Robert Tibshirani, Stanford	stat-learn
R-bloggers	YouTube-Lectures	2014

11.	Introduction to Machine Learning	Katie Malone, Sebastian Thrun, Udacity	ML-Udacity	YouTube-Lectures	2015
12.	Introduction to Machine Learning	Dhruv Batra, Virginia Tech	ECE-5984	YouTube-Lectures	2015
13.	Statistical Learning - Classification	Ali Ghodsi, University of Waterloo	STAT-441	YouTube-Lectures	2015
14.	Machine Learning Theory	Shai Ben-David, University of Waterloo	`None`	YouTube-Lectures	2015
15.	Introduction to Machine Learning	Alex Smola, CMU	10-701	YouTube-Lectures	S2015
16.	Statistical Machine Learning	Larry Wasserman, CMU	`None`	YouTube-Lectures	S2015
17.	ML: Supervised Learning	Michael Littman, Charles Isbell, Pushkar Kolhe, GaTech	ML-Udacity	YouTube-Lectures	2015
18.	ML: Unsupervised Learning	Michael Littman, Charles Isbell, Pushkar Kolhe, GaTech	ML-Udacity	YouTube-Lectures	2015
19.	Advanced Introduction to Machine Learning	Barnabas Poczos and Alex Smola	10-715	YouTube-Lectures	F2015
20.	Machine Learning	Pedro Domingos, UWashington	CSEP-546	YouTube-Lectures	S2016

21.	Statistical Machine Learning	Larry Wasserman, CMU	`None`	YouTube-Lectures	S2016
22.	Machine Learning with Large Datasets	William Cohen, CMU	10-605	YouTube-Lectures	F2016
23.	Math Background for Machine Learning	Geoffrey Gordon, CMU	`10-600`	YouTube-Lectures	F2016
24.	Statistical Learning - Classification	Ali Ghodsi, University of Waterloo	`None`	YouTube-Lectures	2017
25.	Machine Learning	Andrew Ng, Stanford University	Coursera-ML	YouTube-Lectures	2017
26.	Machine Learning	Roni Rosenfield, CMU	10-601	YouTube-Lectures	2017
27.	Statistical Machine Learning	Ryan Tibshirani, Larry Wasserman, CMU	10-702	YouTube-Lectures	S2017
28.	Machine Learning for Computer Vision	Fred Hamprecht, Heidelberg University	`None`	YouTube-Lectures	F2017
29.	Math Background for Machine Learning	Geoffrey Gordon, CMU	10-606 / 10-607	YouTube-Lectures	F2017
30.	Data Visualization	Ali Ghodsi, University of Waterloo	`None`	YouTube-Lectures	2017

31.	Machine Learning for Physicists	Florian Marquardt, Uni Erlangen-Nürnberg	ML4Phy-17	Lecture-Videos	2017
32.	Machine Learning for Intelligent Systems	Kilian Weinberger, Cornell University	CS4780	YouTube-Lectures	F2018
33.	Statistical Learning Theory and Applications	Tomaso Poggio, Lorenzo Rosasco, Sasha Rakhlin	9.520/6.860	YouTube-Lectures	F2018
34.	Machine Learning and Data Mining	Mike Gelbart, University of British Columbia	CPSC-340	YouTube-Lectures	2018
35.	Foundations of Machine Learning	David Rosenberg, Bloomberg	FOML	YouTube-Lectures	2018
36.	Introduction to Machine Learning	Andreas Krause, ETH Zürich	IntroML	YouTube-Lectures	2018
37.	Machine Learning Fundamentals	Sanjoy Dasgupta, UC-San Diego	MLF-slides	YouTube-Lectures	2018
38.	Machine Learning	Jordan Boyd-Graber, University of Maryland	CMSC-726	YouTube-Lectures	2015-2018
39.	Machine Learning	Andrew Ng, Stanford University	CS229	YouTube-Lectures	2018
40.	Machine Intelligence	H.R.Tizhoosh, UWaterloo	SYDE-522	YouTube-Lectures	2019

41.	Introduction to Machine Learning	Pascal Poupart, University of Waterloo	CS480/680	YouTube-Lectures	S2019
42.	Advanced Machine Learning	Thorsten Joachims, Cornell University	CS-6780	Lecture-Videos	S2019
43.	Machine Learning for Structured Data	Matt Gormley, Carnegie Mellon University	10-418/10-618	YouTube-Lectures	F2019
44.	Advanced Machine Learning	Joachim Buhmann, ETH Zürich	ML2-AML	Lecture-Videos	F2019
45.	Machine Learning for Signal Processing	Vipul Arora, IIT-Kanpur	MLSP	Lecture-Videos	F2019
46.	Foundations of Machine Learning	Animashree Anandkumar, CalTech	CMS-165	YouTube-Lectures	2019
47.	Machine Learning for Physicists	Florian Marquardt, Uni Erlangen-Nürnberg	`None`	Lecture-Videos	2019
48.	Applied Machine Learning	Andreas Müller, Columbia University	COMS-W4995	YouTube-Lectures	2019
49.	Fundamentals of Machine Learning over Networks	Hossein Shokri-Ghadikolaei, KTH, Sweden	MLoNs	YouTube-Lectures	2019
50.	Foundations of Machine Learning and Statistical Inference	Animashree Anandkumar, CalTech	CMS-165	YouTube-Lectures	2020

51.	Machine Learning	Rebecca Willett and Yuxin Chen, University of Chicago	STAT 37710 / CMSC 35400	Lecture-Videos	S2020
52.	Introduction to Machine Learning	Sanjay Lall and Stephen Boyd, Stanford University	EE104/CME107	YouTube-Lectures	S2020
53.	Applied Machine Learning	Andreas Müller, Columbia University	COMS-W4995	YouTube-Lectures	S2020
54.	Statistical Machine Learning	Ulrike von Luxburg, Eberhard Karls Universität Tübingen	Stat-ML	YouTube-Lectures	SS2020
55.	Probabilistic Machine Learning	Philipp Hennig, Eberhard Karls Universität Tübingen	Prob-ML	YouTube-Lectures	SS2020
56.	Machine Learning	Sarath Chandar, PolyMTL, UdeM, Mila	INF8953CE	YouTube-Lectures	F2020
57.	Machine Learning	Erik Bekkers, Universiteit van Amsterdam	UvA-ML	YouTube-Lectures	F2020
58.	Neural Networks for Signal Processing	Shayan Srinivasa Garani, Indian Institute of Science	NN4SP	YouTube-Lectures	F2020
59.	Introduction to Machine Learning	Dmitry Kobak, Universität Klinikum Tübingen	`None`	YouTube-Lectures	2020
60.	Machine Learning (PRML)	Erik J. Bekkers, Universiteit van Amsterdam	UvAML-1	YouTube-Lectures	2020

61.	Machine Learning with Kernel Methods	Julien Mairal and Jean-Philippe Vert, Inria/ENS Paris-Saclay, Google	ML-Kernels	YouTube-Lectures	S2021
62.	Continual Learning	Vincenzo Lomonaco, Università di Pisa	ContLearn'21	YouTube-Lectures	2021
63.	Causality	Christina Heinze-Deml, ETH Zurich	Causal'21	YouTube-Lectures	2021

Go to Contents :arrow_heading_up:

:balloon: Reinforcement Learning :hotsprings: :video_game:

S.No	Course Name	University/Instructor(s)	Course Webpage	Video Lectures	Year
1.	A Short Course on Reinforcement Learning	Satinder Singh, UMichigan	`None`	YouTube-Lectures	2011
2.	Approximate Dynamic Programming	Dimitri P. Bertsekas, MIT	Lecture-Slides	YouTube-Lectures	2014
3.	Introduction to Reinforcement Learning	David Silver, DeepMind	UCL-RL	YouTube-Lectures	2015
4.	Reinforcement Learning	Charles Isbell, Chris Pryby, GaTech; Michael Littman, Brown	RL-Udacity	YouTube-Lectures	2015
5.	Reinforcement Learning	Balaraman Ravindran, IIT Madras	RL-IITM	YouTube-Lectures	2016
6.	Deep Reinforcement Learning	Sergey Levine, UC Berkeley	CS-294	YouTube-Lectures	S2017
7.	Deep Reinforcement Learning	Sergey Levine, UC Berkeley	CS-294	YouTube-Lectures	F2017
8.	Deep RL Bootcamp	Many legends, UC Berkeley	Deep-RL	YouTube-Lectures	2017
9	Data Efficient Reinforcement Learning	Lots of Legends, Canary Islands	DERL-17	YouTube-Lectures	2017
10.	Deep Reinforcement Learning	Sergey Levine, UC Berkeley	CS-294-112	YouTube-Lectures	2018

11.	Reinforcement Learning	Pascal Poupart, University of Waterloo	CS-885	YouTube-Lectures	2018
12.	Deep Reinforcement Learning and Control	Katerina Fragkiadaki and Tom Mitchell, CMU	10-703	YouTube-Lectures	2018
13.	Reinforcement Learning and Optimal Control	Dimitri Bertsekas, Arizona State University	RLOC	Lecture-Videos	2019
14.	Reinforcement Learning	Emma Brunskill, Stanford University	CS 234	YouTube-Lectures	2019
15.	Reinforcement Learning Day	Lots of Legends, Microsoft Research, New York	RLD-19	YouTube-Lectures	2019
16.	New Directions in Reinforcement Learning and Control	Lots of Legends, IAS, Princeton University	NDRLC-19	YouTube-Lectures	2019
17.	Deep Reinforcement Learning	Sergey Levine, UC Berkeley	CS 285	YouTube-Lectures	F2019
18.	Deep Multi-Task and Meta Learning	Chelsea Finn, Stanford University	CS 330	YouTube-Lectures	F2019
19.	RL-Theory Seminars	Lots of Legends, Earth	RL-theory-sem	YouTube-Lectures	2020 -
20.	Deep Reinforcement Learning	Sergey Levine, UC Berkeley	CS 285	YouTube-Lectures	F2020

21.	Introduction to Reinforcement Learning	Amir-massoud Farahmand, Vector Institute, University of Toronto	RL-intro	YouTube-Lectures	S2021
22.	Reinforcement Learning	Antonio Celani and Emanuele Panizon, International Centre for Theoretical Physics	`None`	YouTube-Lectures	2021
23.	Computational Sensorimotor Learning	Pulkit Agrawal, MIT-CSAIL	6.884-CSL	YouTube-Lectures	S2021
24.	Reinforcement Learning	Dimitri P. Bertsekas, ASU/MIT	RL-21	YouTube-Lectures	S2021
25.	Reinforcement Learning	Sarath Chandar, École Polytechnique de Montréal	INF8953DE	YouTube-Lectures	F2021
26.	Deep Reinforcement Learning	Sergey Levine, UC Berkeley	CS 285	YouTube-Lectures	F2021
27.	Reinforcement Learning Lecture Series	Lots of Legends, DeepMind & UC London	RL-series	YouTube-Lectures	2021
28.	Reinforcement Learning	Dimitri P. Bertsekas, ASU/MIT	RL-22	YouTube-Lectures	S2022
29.	Deep Reinforcement Learning	Sergey Levine, UC Berkeley	CS 285	YouTube-Lectures	F2022
30.	Deep Reinforcement Learning	Sergey Levine, UC Berkeley	CS 285	YouTube-Lectures	F2023
31.	Deep Reinforcement Learning	Sergey Levine, UC Berkeley	CS 185/285	YouTube-Lectures	S2026

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:loudspeaker: Probabilistic Graphical Models :sparkles:

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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:game_die: Bayesian Deep Learning :spades: :gem:

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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:movie_camera: Medical Imaging :camera: :video_camera:

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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:tada: Graph Neural Networks (Geometric DL) :confetti_ball: :balloon:

S.No	Course Name	University/Instructor(s)	Course WebPage	Lecture Videos	Year
1.	Deep learning on graphs and manifolds	Michael Bronstein, Technion	`None`	YouTube-Lectures	2017
2.	Geometric Deep Learning on Graphs and Manifolds	Michael Bronstein, Technische Universität München	`None`	Lec-part1,
Lec-part2	2017
3.	Eurographics Symposium on Geometry Processing - Graduate School	Lots of Legends, SIGGRAPH, London	SGP-2017	YouTube-Lectures	2017
4.	Eurographics Symposium on Geometry Processing - Graduate School	Lots of Legends, SIGGRAPH, Paris	SGP-2018	YouTube-Lectures	2018
5.	Analysis of Networks: Mining and Learning with Graphs	Jure Leskovec, Stanford University	CS224W	Lecture-Videos	2018
6.	Machine Learning with Graphs	Jure Leskovec, Stanford University	CS224W	YouTube-Lectures	2019
7.	Geometry and Learning from Data in 3D and Beyond -Geometry and Learning from Data Tutorials	Lots of Legends, IPAM UCLA	GLDT	Lecture-Videos	2019
8.	Geometry and Learning from Data in 3D and Beyond - Geometric Processing	Lots of Legends, IPAM UCLA	GeoPro	Lecture-Videos	2019
9.	Geometry and Learning from Data in 3D and Beyond - Shape Analysis	Lots of Legends, IPAM UCLA	Shape-Analysis	Lecture-Videos	2019
10.	Geometry and Learning from Data in 3D and Beyond - Geometry of Big Data	Lots of Legends, IPAM UCLA	Geo-BData	Lecture-Videos	2019

11.	Geometry and Learning from Data in 3D and Beyond - Deep Geometric Learning of Big Data and Applications	Lots of Legends, IPAM UCLA	DGL-BData	Lecture-Videos	2019
12.	Israeli Geometric Deep Learning	Lots of Legends, Israel	iGDL-20	Lecture-Videos	2020
13.	Machine Learning for Graphs and Sequential Data	Stephan Günnemann, Technische Universität München (TUM)	MLGS-20	Lecture-Videos	S2020
14.	Machine Learning with Graphs	Jure Leskovec, Stanford	CS224W	YouTube-Lectures	W2021
15.	Geometric Deep Learning - AMMI	Lots of Legends, Virtual	GDL-AMMI	YouTube-Lectures	2021
16.	Summer School on Geometric Deep Learning -	Lots of Legends, DTU, DIKU & AAU	GDL- DTU, DIKU & AAU	Lecture-Videos	2021
17.	Graph Neural Networks	Alejandro Ribeiro, University of Pennsylvania	ESE 514	YouTube-Lectures	F2021

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:hibiscus: Natural Language Processing :cherry_blossom: :sparkling_heart:

S.No	Course Name	University/Instructor(s)	Course WebPage	Lecture Videos	Year
1.	Computational Linguistics I	Jordan Boyd-Graber, University of Maryland	CMS-723	YouTube-Lectures	2013-2018
2.	Deep Learning for Natural Language Processing	Nils Reimers, TU Darmstadt	DL4NLP	YouTube-Lectures	2015-2017
3.	Deep Learning for Natural Language Processing	Many Legends, DeepMind-Oxford	DL-NLP	YouTube-Lectures	2017
4.	Deep Learning for Speech & Language	UPC Barcelona	DL-SL	Lecture-Videos	2017
5.	Neural Networks for Natural Language Processing	Graham Neubig, CMU	NN4NLP Code	YouTube-Lectures	2017
6.	Neural Networks for Natural Language Processing	Graham Neubig, CMU	NN4-NLP	YouTube-Lectures	2018
7.	Deep Learning for NLP	Min-Yen Kan, NUS	CS-6101	YouTube-Lectures	2018
8.	Neural Networks for Natural Language Processing	Graham Neubig, CMU	NN4NLP	YouTube-Lectures	2019
9.	Natural Language Processing with Deep Learning	Abigail See, Chris Manning, Richard Socher, Stanford University	CS224n	YouTube-Lectures	2019
10.	Natural Language Understanding	Bill MacCartney and Christopher Potts	CS224U	YouTube-Lectures	S2019

11.	Neural Networks for Natural Language Processing	Graham Neubig, Carnegie Mellon University	CS 11-747	YouTube-Lectures	S2020
12.	Advanced Natural Language Processing	Mohit Iyyer, UMass Amherst	CS 685	YouTube-Lectures	F2020
13.	Machine Translation	Philipp Koehn, Johns Hopkins University	EN 601.468/668	YouTube-Lectures	F2020
14.	Neural Networks for NLP	Graham Neubig, Carnegie Mellon University	CS 11-747	YouTube-Lectures	2021
15.	Deep Learning for Natural Language Processing	Kyunghyun Cho, New York University	DS-GA 1011	YouTube-Lectures	F2021
16.	Natural Language Processing with Deep Learning	Chris Manning, Stanford University	CS224n	YouTube-Lectures	2021

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:heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_s

deep-learning-drizzle

:balloon: :tada: Deep Learning Drizzle :confetti_ball: :balloon:

Contents

:tada: Deep Learning (Deep Neural Networks) :confetti_ball: :balloon:

:cupid: Machine Learning Fundamentals :cyclone: :boom:

:cupid: Optimization for Machine Learning :cyclone: :boom:

:cupid: General Machine Learning :cyclone: :boom:

:balloon: Reinforcement Learning :hotsprings: :video_game:

:loudspeaker: Probabilistic Graphical Models :sparkles:

:game_die: Bayesian Deep Learning :spades: :gem:

:movie_camera: Medical Imaging :camera: :video_camera:

:tada: Graph Neural Networks (Geometric DL) :confetti_ball: :balloon:

:hibiscus: Natural Language Processing :cherry_blossom: :sparkling_heart: