List of Top Artificial Intelligence, Machine Learning, Deep Learning Video Lectures
Linear AlgebraGilbert Strang, MIT
Probability PrimerBrown University
Information Theory, Pattern Recognition, and Neural NetworksUniversity of Cambridge
Linear Algebra ReviewCMU
Probability and StatisticsProbability and Statistics
Linear Algebra: An in-depth Introduction Part 1Pavel Grinfeld
Linear Algebra: An in-depth Introduction Part 2Pavel Grinfeld
Linear Algebra: An in-depth Introduction Part 3Pavel Grinfeld
Linear Algebra: An in-depth Introduction Part 4Pavel Grinfeld
Multivariable CalculusKhan Academy
Essence of Linear AlgebraGrant Sanderson
Essence of CalculusGrant Sanderson
Mathematics for Machine Learning : Linear Algebra, CalculusDavid Dye, Samuel Cooper, and Freddie Page, IC-London
Multivariable Calculus: IITRS.K. Gupta and Sanjeev Kumar, IIT-Roorkee
Engineering ProbabilityRich Radke, Rensselaer Polytechnic Institute
Matrix Methods in Data Analysis, Signal Processing, and Machine LearningGilbert Strang, MIT
An Introduction to Statistical Learning with Applications in RTrevor Hastie and Robert Tibshirani, Stanford
Statistical Machine Learning: CMURyan Tibshirani, Larry Wasserman, CMU
Math Background for Machine Learning: CMUGeoffrey Gordon, CMU
Statistical Learning Theory and ApplicationsTomaso Poggio, Lorenzo Rosasco, Sasha Rakhlin
Probabilistic Modeling and Machine LearningZoubin Ghahramani, University of Cambridge
Probabilistic Graphical Models: CMUEric Xing, CMU
Learning with Structured Data: An Introduction to Probabilistic Graphical ModelsChristoph Lampert, IST Austria
Probabilistic Graphical Models: University of Notre DameNicholas Zabaras, University of Notre Dame
CS221 Artificial Intelligence (Stanford)
CS229: Machine LearningAndrew Ng, Stanford University
Machine LearningMachine Learning
Machine Learning CMUTom Mitchell, CMU
Machine Learning and Data MiningNando de Freitas, University of British Columbia
Learning from DataYaser Abu-Mostafa, CalTech
Machine Learning: Technische Universität MünchenRudolph Triebel, Technische Universität München
Introduction to Machine Learning: CMUAlex Smola, CMU
Pattern RecognitionSukhendu Das, IIT-M and C.A. Murthy, ISI-Calcutta- An Introduction to Statistical Learning with Applications in RTrevor Hastie and Robert Tibshirani, Stanford
Introduction to Machine Learning: UdacityKatie Malone, Sebastian Thrun, Udacity
Introduction to Machine Learning: Virginia TechDhruv Batra, Virginia Tech
Statistical Learning - ClassificationAli Ghodsi, University of Waterloo
Machine Learning TheoryShai Ben-David, University of Waterloo
Introduction to Machine Learning:-CMUAlex Smola, CMU
ML: Supervised LearningMichael Littman, Charles Isbell, Pushkar Kolhe, GaTech
ML: Unsupervised LearningMichael Littman, Charles Isbell, Pushkar Kolhe, GaTech
Machine Learning: UWPedro Domingos, UWashington
Statistical Machine LearningLarry Wasserman, CMU
Machine Learning with Large DatasetsWilliam Cohen, CMU
Math Background for Machine LearningGeoffrey Gordon, CMU
Statistical Learning :- ClassificationAli Ghodsi, University of Waterloo
Machine Learning: StanfordAndrew Ng, Stanford University- Statistical Machine Learning: CMURyan Tibshirani, Larry Wasserman, CMU
Machine Learning for Computer VisionFred Hamprecht, Heidelberg University- Math Background for Machine Learning: CMUGeoffrey Gordon, CMU
Data VisualizationAli Ghodsi, University of Waterloo
Machine Learning for Intelligent SystemsKilian Weinberger, Cornell University- Statistical Learning Theory and ApplicationsTomaso Poggio, Lorenzo Rosasco, Sasha Rakhlin
Machine Learning & Data MiningMike Gelbart, University of British Columbia
Foundations of Machine LearningDavid Rosenberg, Bloomberg
Introduction to Machine LearningAndreas Krause, ETH Zürich
Machine Learning FundamentalsSanjoy Dasgupta, UC-San Diego
Machine Learning: MarylandJordan Boyd-Graber, University of Maryland
Machine IntelligenceH.R.Tizhoosh, UWaterloo
Introduction to Machine Learning: University of WaterlooPascal Poupart, University of Waterloo
Advanced Machine LearningThorsten Joachims, Cornell University
Machine Learning for Structured DataMatt Gormley, Carnegie Mellon University
Advanced Machine Learning: ETHJoachim Buhmann, ETH Zürich
Machine Learning for Signal ProcessingMachine Learning for Signal Processing
Learn_Machine_Learning_in_3_Months Siraj Raval 
Probabilistic Graphical ModelsMany Legends, MPI-IS
Convex OptimizationStephen Boyd, Stanford University
Introduction to OptimizationMichael Zibulevsky, Technion
Optimization for Machine LearningS V N Vishwanathan, Purdue University
OptimizationGeoff Gordon & Ryan Tibshirani, CMU
Convex Optimization: IITJoydeep Dutta, IIT-Kanpur
Foundations of OptimizationJoydeep Dutta, IIT-Kanpur
Algorithmic Aspects of Machine LearningAnkur Moitra, MIT
Numerical OptimizationShirish K. Shevade, IISC
Advanced AlgorithmsAnkur Moitra, MIT
Introduction to Optimization: TechnionMichael Zibulevsky, Technion
Convex Optimization..Javier Peña & Ryan Tibshirani
Modern Algorithmic Optimization: UCLouvainYurii Nesterov, UCLouvain
Natural Language Processing with Deep Learning: StanfordStanford CS224n
Short Course on Reinforcement LearningSatinder Singh, UMichigan
Approximate Dynamic ProgrammingDimitri P. Bertsekas, MIT
Introduction to Reinforcement LearningDavid Silver, DeepMind
Reinforcement Learning: GaTechCharles Isbell, Chris Pryby, GaTech; Michael Littman, Brown
Deep Reinforcement Learning: UC BerkeleySergey Levine, UC Berkeley 
Data Efficient Reinforcement LearningLots of Legends, Canary Islands
Deep Reinforcement LearningSergey Levine, UC Berkeley
Reinforcement Learning: University of WaterlooPascal Poupart, University of Waterloo
Reinforcement Learning: Stanford UniversityEmma Brunskill, Stanford University
Reinforcement Learning DayMicrosoft Research, New York
New Directions in Reinforcement Learning and ControlLots of Legends, IAS, Princeton University
CS285: Deep Reinforcement Learning, UC Berkeley | Fall 2019
- CS230 Deep Learning (Stanford)
Neural Networks for Machine LearningGeoffrey Hinton, University of Toronto
Neural Networks DemystifiedStephen Welch, Welch Labs
Deep Learning at OxfordNando de Freitas, Oxford University
Deep Learning for PerceptionDhruv Batra, Virginia Tech
Deep LearningAli Ghodsi, University of Waterloo
CS231n: CNNs for Visual RecognitionAndrej Karpathy, Stanford University
CS224d: Deep Learning for NLPRichard Socher, Stanford University
Bay Area Deep LearningMany legends, Stanford
CS231n: CNNs for Visual Recognition.Andrej Karpathy, Stanford University
Neural Networks:Université de SherbrookeHugo 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 CourseLeo Isikdogan, UT Austin
Deep Learning and its ApplicationsFrançois Pitié, Trinity College Dublin
Deep Learning: Andrew Ng, Stanford UniversityAndrew Ng, Stanford University
UvA Deep LearningEfstratios Gavves, University of Amsterdam
Advanced Deep Learning and Reinforcement Learning DeepMind
Machine Learning: Universiteit AmsterdamPeter Bloem, Vrije Universiteit Amsterdam- CS285: Deep Reinforcement Learning, UC Berkeley | Fall 2019
Deep Learning EPFLFrancois Fleuret, EPFL
Introduction to Deep Learning MITAlexander Amini, Harini Suresh and others, c
Deep Learning for Self-Driving CarsLex Fridman, MIT
Introduction to Deep Learning CMUBhiksha Raj , CMU
Deep Learning SpecializationAndrew Ng, Stanford
Deep Learning University of WaterlooAli Ghodsi, University of Waterloo
Deep Learning IIT-MadrasMitesh Khapra, IIT-Madras
Deep Learning for AIDeep Learning for AI- MIT Deep LearningLex Fridman, MIT
Deep Learning Book companion videosIan Goodfellow 
Theories of Deep LearningStanford
Neural Networks Grant SandersonGrant Sanderson- CS230: Deep Learning StanfordAndrew Ng, Kian Katanforoosh, Stanford
Theory of Deep Learning Canary IslandsCanary Islands
Introduction to Deep LearningAlex Smola, UC Berkeley
Deep Unsupervised LearningPieter Abbeel, UC Berkeley
Machine Learning Universiteit AmsterdamPeter Bloem, Vrije Universiteit Amsterdam
Deep Learning IIT KgpPrabir Kumar Biswas, IIT Kgp
Deep Learning and its Applications IIT MandiIIT Mandi
Neural Networks Harvey Mudd CollegeNeil Rhodes, Harvey Mudd College
Deep Learning ETH ZürichThomas Hofmann, ETH Zürich
Deep Learning Charles UniversityMilan Straka, Charles University
Deep Learning Foundations and Applications IIT-KgpIIT-Kgp
CMU Neural Nets for NLP 2020 (17): Adversarial Methods
CMU Neural Nets for NLP 2020 (5): Efficiency Tricks for Neural Nets
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Deep Learning for Speech & LanguageUPC Barcelona
Speech and Audio in the NortheastMany Legends, Google NYC
Automatic Speech RecognitionSamudra Vijaya K, TIFR
Speech and Audio in the Northeast-17Many Legends, Google NYC
Speech and Audio in the Northeast-18Google Cambridge
Deep Learning for Speech RecognitionAoE