Machine Learning and data mining are getting more and more hot in recent year, under the background, many of machine learning classes now are available online. People who are interested in the topics found a lot of lectures cover similar things, but maybe in different level, focus or point of view. So it may not be a bad idea to make a collection of such classes and sort things out a little bit. (Noted that most of the courses listed below have video online, and links attached may not be the newest version of the classes as time goes by; this post will keep updating if I find something more)
- Nando de Freitas‘s machine learning class, content is quite complete compared to others, and a little more bayesian, great lecturing – https://www.youtube.com/playlist?list=PLE6Wd9FR–EdyJ5lbFl8UuGjecvVw66F6
- Nando de Freitas’s machine learning class at undergraduate (introductionary) level – https://www.youtube.com/playlist?list=PLE6Wd9FR–Ecf_5nCbnSQMHqORpiChfJf
- Andrew Ng’s machine learning class, most popular one I guess, you can find it in youtube or coursera.
- Yaser Abu-Mostafa’s machine learning class, has a good way of explaining basic learning theory – http://work.caltech.edu/telecourse.html
- Alex Smola and Geoffery Gordon’s machine learning class, not very complete, but Alex has some good in depth explaination about the topics (while Geoff has some clear and more detailed explaination about things) – http://alex.smola.org/teaching/cmu2013-10-701x/index.html
Advanced machine learning topics:
- Eric Xing’s probabilistic graphical model class – http://www.cs.cmu.edu/~epxing/Class/10708/index.html