Stanford Machine Learning course

I'm in a Machine Learning class right now and the math is absurd. Our professor goes over general theory in class and references some practical applications. For homework we have to prove the theories and then create MATLAB programs that create the figures in our textbook (Bishop). It's pretty dense and time consuming.<p>However, I come from a Physics background, so the math is just different, and may in fact be easier. However, I've been referencing my Mathematical Methods for Physicists book (Boas) pretty often to brush up on some linear algebra techniques.<p>Lastly, its been said quite a bit in these forums, but if you are just interested in implementing this stuff, definitely check out Collective Intelligence by Oreilly. I use that book in tandem to bring some of the high-level concepts back down to earth. :)<p>Good luck

You can see example final projects for this course here... <a href="http://www.stanford.edu/class/cs229/projects2007.html" rel="nofollow">http://www.stanford.edu/class/cs229/projects2007.html</a><p>The midterm for CS 229 was probably the hardest exam I took in college.

I read some of the ML lectures notes from OCW some time back. It was very math heavy. Is that an MIT thing or is that how ML is taught in all universities?

The first lecture in this series mentions that the discussion groups are also available via video. Has anyone found these?