Online resources to study machine learning

I have been collecting information on online resources for machine learning. I share them as it will surely help several grad students. If you have some interesting ML links please do post them in the comments.

Definition

• Many people are not clear about the boundaries (as flimsy as they may) between traditional AI, Neuroscience and Machine Learning. This introduction by Tom Michell ought to help.

Where to study from?

• Lectures by Andrew Ng (Stanford) are said to be a good start.
• Hundreds more of ML lectures are available at videolectures.net. These videos are good to learn more about topics that you are interested in.
• Long list of textbooks to read (links 1 and 2). This text is free online.

How to keep up with the field?

• ML is a fast changing field and you need to update yourself with the latest papers. The journals/conferences I found to be relevant are:

1. JMLR
2. ICML
3. NIPS
4. COLT
5. Pattern Recognition
6. TPAMI
7. Machine Learning

• I found these blogs to be useful too

1. http://hunch.net/
2. http://www.reddit.com/r/MachineLearning/
3. http://metaoptimize.com/qa/
   
4. http://mark.reid.name/iem
5. CIML

Programming tools

• Not everyone can afford Matlab. Fortunately Python (with its Numpy extension) can be used as a substitute.
• Before implementing any standard algorithm look for online ML libraries with it. (tip: some libraries work only on *nix environments)

Machine learning has a lot in common with statistics, in fact it can be said to be derived from statistics. This link has a funny take on why ML is more popular.

Addendum (06/01/2010)

Here are some links to online lecture videos on related topics. There might be other course videos out there. Do your own search if you have time.

• Calculus: Several course videos here. My pick
• Linear Algebra: The famous MIT course by Gilbert Strang
• Probability theorem: From UCLA
• Convex optimization: Stanford lectures by Stephen Boyd. Part 2 of the course is available on youtube