List of Top Artificial Intelligence, Machine Learning, Deep Learning Video Lectures
Gilbert Strang, MIT Brown University University of Cambridge CMU Probability and Statistics Pavel Grinfeld Pavel Grinfeld Pavel Grinfeld Pavel Grinfeld Khan Academy Grant Sanderson Grant Sanderson David Dye, Samuel Cooper, and Freddie Page, IC-London S.K. Gupta and Sanjeev Kumar, IIT-Roorkee Rich Radke, Rensselaer Polytechnic Institute Gilbert Strang, MIT Trevor Hastie and Robert Tibshirani, Stanford Ryan Tibshirani, Larry Wasserman, CMU Geoffrey Gordon, CMU Tomaso Poggio, Lorenzo Rosasco, Sasha Rakhlin Zoubin Ghahramani, University of Cambridge Eric Xing, CMU Christoph Lampert, IST Austria Nicholas Zabaras, University of Notre Dame Andrew Ng, Stanford University Machine Learning Tom Mitchell, CMU Nando de Freitas, University of British Columbia Yaser Abu-Mostafa, CalTech Rudolph Triebel, Technische Universität München Alex Smola, CMU Sukhendu Das, IIT-M and C.A. Murthy, ISI-Calcutta Trevor Hastie and Robert Tibshirani, Stanford Katie Malone, Sebastian Thrun, Udacity Dhruv Batra, Virginia Tech Ali Ghodsi, University of Waterloo Shai Ben-David, University of Waterloo Alex Smola, CMU Michael Littman, Charles Isbell, Pushkar Kolhe, GaTech Michael Littman, Charles Isbell, Pushkar Kolhe, GaTech Pedro Domingos, UWashington Larry Wasserman, CMU William Cohen, CMU Geoffrey Gordon, CMU Ali Ghodsi, University of Waterloo Andrew Ng, Stanford University Ryan Tibshirani, Larry Wasserman, CMU Fred Hamprecht, Heidelberg University Geoffrey Gordon, CMU Ali Ghodsi, University of Waterloo Kilian Weinberger, Cornell University Tomaso Poggio, Lorenzo Rosasco, Sasha Rakhlin Mike Gelbart, University of British Columbia David Rosenberg, Bloomberg Andreas Krause, ETH Zürich Sanjoy Dasgupta, UC-San Diego Jordan Boyd-Graber, University of Maryland H.R.Tizhoosh, UWaterloo Pascal Poupart, University of Waterloo Thorsten Joachims, Cornell University Matt Gormley, Carnegie Mellon University Joachim Buhmann, ETH Zürich Machine Learning for Signal Processing Siraj Raval Many Legends, MPI-IS Stephen Boyd, Stanford University Michael Zibulevsky, Technion S V N Vishwanathan, Purdue University Geoff Gordon & Ryan Tibshirani, CMU Joydeep Dutta, IIT-Kanpur Joydeep Dutta, IIT-Kanpur Ankur Moitra, MIT Shirish K. Shevade, IISC Ankur Moitra, MIT Michael Zibulevsky, Technion Javier Peña & Ryan Tibshirani Yurii Nesterov, UCLouvain Stanford CS224n Satinder Singh, UMichigan Dimitri P. Bertsekas, MIT David Silver, DeepMind Charles Isbell, Chris Pryby, GaTech; Michael Littman, Brown Sergey Levine, UC Berkeley Lots of Legends, Canary Islands Sergey Levine, UC Berkeley Pascal Poupart, University of Waterloo Emma Brunskill, Stanford University Microsoft Research, New York Lots of Legends, IAS, Princeton University Geoffrey Hinton, University of Toronto Stephen Welch, Welch Labs Nando de Freitas, Oxford University Dhruv Batra, Virginia Tech Ali Ghodsi, University of Waterloo Andrej Karpathy, Stanford University Richard Socher, Stanford University Many legends, Stanford Andrej Karpathy, Stanford University Hugo Larochelle, Université de Sherbrooke Richard Socher, Stanford University Justin Johnson, Stanford University Leo Isikdogan, UT Austin François Pitié, Trinity College Dublin Andrew Ng, Stanford University Efstratios Gavves, University of Amsterdam DeepMind Peter Bloem, Vrije Universiteit Amsterdam Francois Fleuret, EPFL Alexander Amini, Harini Suresh and others, c Lex Fridman, MIT Bhiksha Raj , CMU Andrew Ng, Stanford Ali Ghodsi, University of Waterloo Mitesh Khapra, IIT-Madras Deep Learning for AI Lex Fridman, MIT Ian Goodfellow Stanford Grant Sanderson Andrew Ng, Kian Katanforoosh, Stanford Canary Islands Alex Smola, UC Berkeley Pieter Abbeel, UC Berkeley Peter Bloem, Vrije Universiteit Amsterdam Prabir Kumar Biswas, IIT Kgp IIT Mandi Neil Rhodes, Harvey Mudd College Thomas Hofmann, ETH Zürich Milan Straka, Charles University IIT-Kgp
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Linear Algebra Gilbert Strang, MIT
Probability Primer Brown University
Information Theory, Pattern Recognition, and Neural Networks University of Cambridge
Linear Algebra Review CMU
Probability and Statistics Probability and Statistics
Linear Algebra: An in-depth Introduction Part 1 Pavel Grinfeld
Linear Algebra: An in-depth Introduction Part 2 Pavel Grinfeld
Linear Algebra: An in-depth Introduction Part 3 Pavel Grinfeld
Linear Algebra: An in-depth Introduction Part 4 Pavel Grinfeld
Multivariable Calculus Khan Academy
Essence of Linear Algebra Grant Sanderson
Essence of Calculus Grant Sanderson
Mathematics for Machine Learning : Linear Algebra, Calculus David Dye, Samuel Cooper, and Freddie Page, IC-London
Multivariable Calculus: IITR S.K. Gupta and Sanjeev Kumar, IIT-Roorkee
Engineering Probability Rich Radke, Rensselaer Polytechnic Institute
Matrix Methods in Data Analysis, Signal Processing, and Machine Learning Gilbert Strang, MIT
An Introduction to Statistical Learning with Applications in R Trevor Hastie and Robert Tibshirani, Stanford
Statistical Machine Learning: CMU Ryan Tibshirani, Larry Wasserman, CMU
Math Background for Machine Learning: CMU Geoffrey Gordon, CMU
Statistical Learning Theory and Applications Tomaso Poggio, Lorenzo Rosasco, Sasha Rakhlin
Probabilistic Modeling and Machine Learning Zoubin Ghahramani, University of Cambridge
Probabilistic Graphical Models: CMU Eric Xing, CMU
Learning with Structured Data: An Introduction to Probabilistic Graphical Models Christoph Lampert, IST Austria
Probabilistic Graphical Models: University of Notre Dame Nicholas Zabaras, University of Notre Dame
CS221 Artificial Intelligence (Stanford)
Machine Learning University (AWS)
CS229: Machine Learning Andrew Ng, Stanford University
Machine Learning Machine Learning
Machine Learning CMU Tom Mitchell, CMU
Machine Learning and Data Mining Nando de Freitas, University of British Columbia
Learning from Data Yaser Abu-Mostafa, CalTech
Machine Learning: Technische Universität München Rudolph Triebel, Technische Universität München
Introduction to Machine Learning: CMU Alex Smola, CMU
Pattern Recognition Sukhendu Das, IIT-M and C.A. Murthy, ISI-Calcutta
Introduction to Machine Learning: Udacity Katie Malone, Sebastian Thrun, Udacity
Introduction to Machine Learning: Virginia Tech Dhruv Batra, Virginia Tech
Statistical Learning - Classification Ali Ghodsi, University of Waterloo
Machine Learning Theory Shai Ben-David, University of Waterloo
Introduction to Machine Learning:-CMU Alex Smola, CMU
ML: Supervised Learning Michael Littman, Charles Isbell, Pushkar Kolhe, GaTech
ML: Unsupervised Learning Michael Littman, Charles Isbell, Pushkar Kolhe, GaTech
Machine Learning: UW Pedro Domingos, UWashington
Statistical Machine Learning Larry Wasserman, CMU
Machine Learning with Large Datasets William Cohen, CMU
Math Background for Machine Learning Geoffrey Gordon, CMU
Statistical Learning :- Classification Ali Ghodsi, University of Waterloo
Machine Learning: Stanford Andrew Ng, Stanford University
Machine Learning for Computer Vision Fred Hamprecht, Heidelberg University
Data Visualization Ali Ghodsi, University of Waterloo
Machine Learning for Intelligent Systems Kilian Weinberger, Cornell University
Machine Learning & Data Mining Mike Gelbart, University of British Columbia
Foundations of Machine Learning David Rosenberg, Bloomberg
Introduction to Machine Learning Andreas Krause, ETH Zürich
Machine Learning Fundamentals Sanjoy Dasgupta, UC-San Diego
Machine Learning: Maryland Jordan Boyd-Graber, University of Maryland
Machine Intelligence H.R.Tizhoosh, UWaterloo
Introduction to Machine Learning: University of Waterloo Pascal Poupart, University of Waterloo
Advanced Machine Learning Thorsten Joachims, Cornell University
Machine Learning for Structured Data Matt Gormley, Carnegie Mellon University
Advanced Machine Learning: ETH Joachim Buhmann, ETH Zürich
Machine Learning for Signal Processing Machine Learning for Signal Processing
Learn_Machine_Learning_in_3_Months Siraj Raval
Probabilistic Graphical Models Many Legends, MPI-IS
Convex Optimization Stephen Boyd, Stanford University
Introduction to Optimization Michael Zibulevsky, Technion
Optimization for Machine Learning S V N Vishwanathan, Purdue University
Optimization Geoff Gordon & Ryan Tibshirani, CMU
Convex Optimization: IIT Joydeep Dutta, IIT-Kanpur
Foundations of Optimization Joydeep Dutta, IIT-Kanpur
Algorithmic Aspects of Machine Learning Ankur Moitra, MIT
Numerical Optimization Shirish K. Shevade, IISC
Advanced Algorithms Ankur Moitra, MIT
Introduction to Optimization: Technion Michael Zibulevsky, Technion
Convex Optimization.. Javier Peña & Ryan Tibshirani
Modern Algorithmic Optimization: UCLouvain Yurii Nesterov, UCLouvain
Natural Language Processing with Deep Learning: Stanford Stanford CS224n
Short Course on Reinforcement Learning Satinder Singh, UMichigan
Approximate Dynamic Programming Dimitri P. Bertsekas, MIT
Introduction to Reinforcement Learning David Silver, DeepMind
Reinforcement Learning: GaTech Charles Isbell, Chris Pryby, GaTech; Michael Littman, Brown
Deep Reinforcement Learning: UC Berkeley Sergey Levine, UC Berkeley
Data Efficient Reinforcement Learning Lots of Legends, Canary Islands
Deep Reinforcement Learning Sergey Levine, UC Berkeley
Reinforcement Learning: University of Waterloo Pascal Poupart, University of Waterloo
Reinforcement Learning: Stanford University Emma Brunskill, Stanford University
Reinforcement Learning Day Microsoft Research, New York
New Directions in Reinforcement Learning and Control Lots of Legends, IAS, Princeton University
CS285: Deep Reinforcement Learning, UC Berkeley | Fall 2019
Deep Learning for Speech & Language UPC Barcelona
Speech and Audio in the Northeast Many Legends, Google NYC
Automatic Speech Recognition Samudra Vijaya K, TIFR
Speech and Audio in the Northeast-17 Many Legends, Google NYC
Speech and Audio in the Northeast-18 Google Cambridge
Deep Learning for Speech Recognition AoE
Neural Networks for Machine Learning Geoffrey Hinton, University of Toronto
Neural Networks Demystified Stephen Welch, Welch Labs
Deep Learning at Oxford Nando de Freitas, Oxford University
Deep Learning for Perception Dhruv Batra, Virginia Tech
Deep Learning Ali Ghodsi, University of Waterloo
CS231n: CNNs for Visual Recognition Andrej Karpathy, Stanford University
CS224d: Deep Learning for NLP Richard Socher, Stanford University
Bay Area Deep Learning Many legends, Stanford
CS231n: CNNs for Visual Recognition. Andrej Karpathy, Stanford University
Neural Networks:Université de Sherbrooke Hugo Larochelle, Université de Sherbrooke
CS224d: Deep Learning for NLP (Stanford) Richard Socher, Stanford University
CS231n: CNNs for Visual Recognition (Stanford) Justin Johnson, Stanford University
Deep Learning Crash Course Leo Isikdogan, UT Austin
Deep Learning and its Applications François Pitié, Trinity College Dublin
Deep Learning: Andrew Ng, Stanford University Andrew Ng, Stanford University
UvA Deep Learning Efstratios Gavves, University of Amsterdam
Advanced Deep Learning and Reinforcement Learning DeepMind
Machine Learning: Universiteit Amsterdam Peter Bloem, Vrije Universiteit Amsterdam
Deep Learning EPFL Francois Fleuret, EPFL
Introduction to Deep Learning MIT Alexander Amini, Harini Suresh and others, c
Deep Learning for Self-Driving Cars Lex Fridman, MIT
Introduction to Deep Learning CMU Bhiksha Raj , CMU
Deep Learning Specialization Andrew Ng, Stanford
Deep Learning University of Waterloo Ali Ghodsi, University of Waterloo
Deep Learning IIT-Madras Mitesh Khapra, IIT-Madras
Deep Learning for AI Deep Learning for AI
Deep Learning Book companion videos Ian Goodfellow
Theories of Deep Learning Stanford
Neural Networks Grant Sanderson Grant Sanderson
Theory of Deep Learning Canary Islands Canary Islands
Introduction to Deep Learning Alex Smola, UC Berkeley
Deep Unsupervised Learning Pieter Abbeel, UC Berkeley
Machine Learning Universiteit Amsterdam Peter Bloem, Vrije Universiteit Amsterdam
Deep Learning IIT Kgp Prabir Kumar Biswas, IIT Kgp
Deep Learning and its Applications IIT Mandi IIT Mandi
Neural Networks Harvey Mudd College Neil Rhodes, Harvey Mudd College
Deep Learning ETH Zürich Thomas Hofmann, ETH Zürich
Deep Learning Charles University Milan Straka, Charles University
Deep Learning Foundations and Applications IIT-Kgp IIT-Kgp
CMU Neural Nets for NLP 2020 (17): Adversarial Methods
CMU Neural Nets for NLP 2020 (5): Efficiency Tricks for Neural Nets
Deep Learning Lecture Summer 2020 (Prof. Andreas Maier)
Advanced Deep Learning for Computer vision (ADL4CV) (IN2364)
