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Comprehensive list of machine learning videos
Machine Learning Courses with Video Lectures
Artificial Intelligence
- CS 188 - Introduction to Artificial Intelligence, UC Berkeley
- 6.034 Artificial Intelligence, MIT OCW
- 15-780 - Graduate Artificial Intelligence, Spring 14, CMU
- CSE 592 Applications of Artificial Intelligence, Winter 2003 - University of Washington
- CS322 - Introduction to Artificial Intelligence, Winter 2012-13 - UBC (YouTube)
- CS 4804: Introduction to Artificial Intelligence, Fall 2016
- CS 5804: Introduction to Artificial Intelligence, Spring 2015
- Artificial Intelligence - IIT Kharagpur
- Artificial Intelligence - IIT Madras
- Artificial Intelligence(Prof.P.Dasgupta) - IIT Kharagpur
- Artificial Intelligence: Knowledge Representation and Reasoning - IIT Madras
- MOOC - Intro to Artificial Intelligence - Udacity
- MOOC - Artificial Intelligence for Robotics - Udacity
- Graduate Course in Artificial Intelligence, Autumn 2012 - University of Washington
- Advanced AI Techniques - WS 2005 - Universität Freiburg (WS 2004)
- Agent-Based Systems 2015/16- University of Edinburgh
- Informatics 2D - Reasoning and Agents 2014/15- University of Edinburgh
- Artificial Intelligence - Hochschule Ravensburg-Weingarten
- Deductive Databases and Knowledge-Based Systems - Technische Universität Braunschweig, Germany
Machine Learning
- Introduction to Machine Learning
- MOOC Machine Learning Andrew Ng - Coursera/Stanford (Notes)
- MOOC - Statistical Learning, Stanford University
- Foundations of Machine Learning Boot Camp, Berkeley Simons Institute
- CS155 - Machine Learning & Data Mining, 2017 - Caltech (Notes) (2016)
- CS 156 - Learning from Data, Caltech
- 10-601 - Introduction to Machine Learning (MS), CMU (YouTube)
- 10-701 - Introduction to Machine Learning (PhD) - Tom Mitchell, Spring 2011, CMU (Fall 2014) (Spring 2015 by Alex Smola)
- Microsoft Research - Machine Learning Course
- CS 446 - Machine Learning, Fall 2016, UIUC(Fall 2015 Lectures)
- undergraduate machine learning at UBC 2012, Nando de Freitas
- CS 229 - Machine Learning - Stanford University
- CS 189/289A Introduction to Machine Learning, Prof Jonathan Shewchuk - UCBerkeley
- CS4780/5780 Machine Learning, Fall 2013 - Cornell University
- CS 5350/6350 - Machine Learning, Fall 2016, University of Utah
- ECE 5984 Introduction to Machine Learning, Spring 2015 - Virginia Tech
- CSx824/ECEx242 Machine Learning, Bert Huang, Fall 2015 - Virginia Tech
- STA 4273H - Large Scale Machine Learning, Winter 2015 - University of Toronto
- CS 485/685 Machine Learning, Shai Ben-David, University of Waterloo
- STAT 441/841 Classification Winter 2017 , Waterloo
- 10-605 - Machine Learning with Large Datasets, Fall 2016 - CMU
- Information Theory, Pattern Recognition, and Neural Networks - University of Cambridge
- Python and machine learning - Stanford Crowd Course Initiative
- MOOC - Machine Learning Part 1a - Udacity/Georgia Tech (Part 1b Part 2 Part 3)
- Machine Learning and Data Mining - WS 2004 - Universität Freiburg
- Machine Learning and Pattern Recognition 2015/16- University of Edinburgh
- Introductory Applied Machine Learning 2015/16- University of Edinburgh
- Pattern Recognition Class (2012)- Universität Heidelberg
- Introduction to Machine Learning - IIT Kharagpur
- Introduction to Machine Learning - IIT Madras
- Pattern Recognition - IISC Bangalore
- Pattern Recognition and Application - IIT Kharagpur
- Pattern Recognition - IIT Madras
- Machine Learning Summer School 2013 - Max Planck Institute for Intelligent Systems Tübingen
- Machine Learning - Professor Kogan (Spring 2016) - Rutgers
- CS273a: Introduction to Machine Learning (YouTube)
- Machine Learning Crash Course 2015
- COM4509/COM6509 Machine Learning and Adaptive Intelligence 2015-16
- Data Mining
- CSEP 546, Data Mining - Pedro Domingos, Sp 2016 - University of Washington (YouTube)
- CS 5140/6140 - Data Mining, Spring 2016, University of Utah (Youtube)
- CS 5955/6955 - Data Mining, University of Utah (YouTube)
- Statistics 202 - Statistical Aspects of Data Mining, Summer 2007 - Google (YouTube)
- MOOC - Text Mining and Analytics by ChengXiang Zhai
- Information Retrieval SS 2014, iTunes - HPI
- MOOC - Data Mining with Weka
- CS 290 DataMining Lectures
- CS246 - Mining Massive Data Sets, Winter 2016, Stanford University (YouTube)
- CAP6673 - Data Mining and Machine Learning - FAU
- Data Warehousing and Data Mining Techniques - Technische Universität Braunschweig, Germany
- Data Science
- Data 8: The Foundations of Data Science - UC Berkeley
- CSE519 - Data Science Fall 2016 - Skiena, SBU
- CS 109 Data Science, Harvard University (YouTube)
- 6.0002 Introduction to Computational Thinking and Data Science - MIT OCW
- Statistics 133 - Concepts in Computing with Data, Fall 2013 - UC Berkeley
- Data Profiling and Data Cleansing (WS 2014/15) - HPI
- AM 207 - Stochastic Methods for Data Analysis, Inference and Optimization, Harvard University
- CS 229r - Algorithms for Big Data, Harvard University (Youtube)
- Algorithms for Big Data - IIT Madras
- Probabilistic Graphical Modeling
- MOOC - Probabilistic Graphical Models - Coursera
- CS 6190 - Probabilistic Modeling, Spring 2016, University of Utah
- 10-708 - Probabilistic Graphical Models, Carnegie Mellon University
- Probabilistic Graphical Models, Daphne Koller, Stanford University
- Probabilistic Models - UNIVERSITY OF HELSINKI
- Probabilistic Modelling and Reasoning 2015/16- University of Edinburgh
- Deep Learning
- 6.S191: Introduction to Deep Learning - MIT
- Deep learning at Oxford 2015 - Nando de Freitas
- 6.S094: Deep Learning for Self-Driving Cars - MIT
- CS294-129 Designing, Visualizing and Understanding Deep Neural Networks (YouTube)
- CS294-112, Deep Reinforcement Learning Sp17 (YouTube)
- UCL Course 2015 on Reinforcement Learning by David Silver from DeepMind (YouTube)
- Deep Learning, Stanford University
- MOOC - Neural Networks for Machine Learning, Geoffrey Hinton 2016 - Coursera
- Stat 946 Deep Learning - University of Waterloo
- Neural networks class - Université de Sherbrooke (YouTube)
- Neural Networks and Applications - IIT Kharagpur
- Practical Deep Learning For Coders
- UVA DEEP LEARNING COURSE
- Nvidia Machine Learning Class
- Advanced Machine Learning
- Machine Learning 2013 - Nando de Freitas, UBC
- Machine Learning, 2014-2015, University of Oxford
- 10-702/36-702 - Statistical Machine Learning - Larry Wasserman, Spring 2016, CMU (Spring 2015)
- 10-715 Advanced Introduction to Machine Learning - CMU (YouTube)
- CS 281B - Scalable Machine Learning, Alex Smola, UC Berkeley
- 18.409 Algorithmic Aspects of Machine Learning Spring 2015 - MIT
- ML based Natural Language Processing and Computer Vision
- CS 224d - Deep Learning for Natural Language Processing, Stanford University (Lectures - Youtube)
- CS 224N - Natural Language Processing, Stanford University
- Natural Language Processing with Deep Learning, Winter 2017, Stanford University
- MOOC - Natural Language Processing, Dan Jurafsky & Chris Manning - Coursera
- MOOC - Natural Language Processing - Coursera, University of Michigan
- CS 231n - Convolutional Neural Networks for Visual Recognition, Stanford University
- Deep Learning for Natural Language Processing, 2017 - Oxford University
- Machine Learning for Robotics and Computer Vision, WS 2013/2014 - TU München (YouTube)
- Informatics 1 - Cognitive Science 2015/16- University of Edinburgh
- Informatics 2A - Processing Formal and Natural Languages 2016-17 - University of Edinburgh
- Computational Cognitive Science 2015/16- University of Edinburgh
- Accelerated Natural Language Processing 2015/16- University of Edinburgh
- Natural Language Processing - IIT Bombay
- Misc Machine Learning Topics
- CS 6955 - Clustering, Spring 2015, University of Utah
- Info 290 - Analyzing Big Data with Twitter, UC Berkeley school of information (YouTube)
- 10-725 Convex Optimization, Spring 2015 - CMU
- CAM 383M - Statistical and Discrete Methods for Scientific Computing, University of Texas
- 9.520 - Statistical Learning Theory and Applications, Fall 2015 - MIT
- Reinforcement Learning - UCL
- Regularization Methods for Machine Learning 2016 (YouTube)
- Statistical Inference in Big Data - University of Toronto
- 10-725 Optimization Fall 2012 - CMU
- 10-801 Advanced Optimization and Randomized Methods - CMU (YouTube)
- Reinforcement Learning 2015/16- University of Edinburgh
- Reinforcement Learning - IIT Madras
- Statistical Rethinking Winter 2015 - Richard McElreath
- Music Information Retrieval - University of Victoria, 2014
- PURDUE Machine Learning Summer School 2011
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