Update (01/24/17): An updated list of video resources is being maintained here.

One feature unique to our field is the sheer amount of online resources. It’s one of the reasons I personally got into machine learning over other disciplines, as self-learning is much more accessible.

This is especially true for video resources. Part of this is because as a field we are very well integrated with computer science, and thus we’re technologically more motivated. (However, arguably no other computer science research domain has as many resources as us.) Whatever the reason, it’s important that we take advantage of all the information available in order to maximize learning.

I haven’t found a good collated list of machine learning videos, so I decided to compile my own. In general the following are excellent resources:

• VideoLectures.NET is the primary video archive for machine learning. Browse the homepage and you’ll see almost all machine learning videos featured on the frontpage, ranging from conferences, workshops, lectures, and even discussions.
• TechTalks.tv is the second most used archive. Notably, many ICML videos are located here.
• Youtube contains a few, such as certain AISTATS and ICLR years. It’s best for collecting lectures, such as by Nando de Freitas and Alex Smola.

Here are all the video archives specific to conferences and workshops that I could find. ¹ Over this winter I’ve been relaxing and marathoning these as if they were TV shows—it’s quite a lot of fun!

• 2015: Neural Information Processing Systems
• 2015: Gaussian Process Summer School, Sheffield
• 2015: Deep Learning Summer School, Montreal
• 2015: Uncertainty in Artificial Intelligence
• 2015: Arthur M. Sackler Colloquia: Drawing Causal Inference from Big Data
• 2015: International Conference on Learning Representations
• 2015: Conference on Learning Theory
• 2015: Machine Learning Summer School, Sydney
• 2014: UToronto Workshop on Big Data and Statistical Machine Learning
• 2014: Neural Information Processing Systems
• 2014: Gaussian Process Summer School, Sheffield
• 2014: Machine Learning Summer School, Pittsburgh
• 2014: International Conference on Machine Learning
• 2014: Conference on Learning Theory
• 2014: Artificial Intelligence and Statistics
• 2014: Gaussian Process Winter School, Sheffield
• 2014: Machine Learning Summer School, Iceland
• 2013: Neural Information Processing Systems
• 2013: International Conference on Machine Learning
• 2013: Machine Learning Summer School, Tuebingen
• 2013: Conference on Learning Theory
• 2013-14: Harvard Institute for Quantitative Social Science, Applied Statistics Workshop
• 2012: Brown ICERM workshops (the 2012 Bayesian nonparametrics is particularly good)
• 2012: Neural Information Processing Systems
• 2012: Uncertainty in Artificial Intelligence
• 2012: International Conference on Machine Learning
• 2012: AAAI Conference on Artificial Intelligence
• 2011: Neural Information Processing Systems
• 2011: Uncertainty in Artificial Intelligence
• 2011: Conference on Learning Theory
• 2011: Artificial Intelligence and Statistics
• 2010: Neural Information Processing Systems
• 2010: International Conference on Machine Learning
• 2010: Artificial Intelligence and Statistics
• 2009: Neural Information Processing Systems
• 2009: Machine Learning Summer School, Cambridge
• 2009: International Conference on Machine Learning
• 2008: Knowledge Discovery and Data Mining
• 2008: Uncertainty in Artificial Intelligence
• 2008: International Conference on Machine Learning
• 2007: International Conference on Machine Learning
• 2006: Neural Information Processing Systems
• 2005: International Conference on Machine Learning

Hope this is of use. I wish I had time to go further and collect resources regarding specific ideas or techniques, such as David Blei’s 2009 lecture on Topic models, or Peter Orbanz’s 2009 lecture on Foundations of Bayesian nonparametrics. Perhaps someone not me will do this in the future.

¹ If you have links to others not listed here and that you feel should be added, or if there is a broken link, please contact me at dustin@cs.columbia.edu. Any help is appreciated and would also help the community at large. :-)