Machine Learning on the Cheap and Easy

I read that one, I liked the fact that he builds up each example from first principles. It's hard to find explanations that bridge theory and practice.

this is like recipes for doing machine learning, not deep enough.

Good stuff - I added http://szeliski.org/Book/ to my reading list, thanks for sharing the link. There's a huge overlap for certain classes of problems. CV in many ways resembles the same problems with large online data streams, noisy, time critical, huge volumes of data - feature extraction is problematic. Hybrid solutions usually required.

I've found that reading books from other ML domains helps out in understanding the application and getting ideas on how to approach the problem.

It's also one of the courses complete with materials in the new iTunes U app.

I found the Stanford course almost assumed too much of a stats background to make it easily accessible. Starting with the math foundations is sound, but scary for people who don't dream in LaTeX :)

also, an HN'er posted his notes on Andrew Ng's class on ML...I learnt more from this than from the videos, as the videos take much too long.

link: http://holehouse.org/mlclass/

I just added it to my list of books to review. Thanks for mentioning it. What did you like about Bishop's Book?

I woundn't consider The Elements of Statistical Learning Theory a (very) basic book. It covers plenty of material in relatively good depth.