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For comparison, here is MIT 9.520:

Statistical Learning Theory and Applications http://www.mit.edu/~9.520/

I think it is interesting how different ML courses can have such different emphasis in content. The MIT course is all about regularization.

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I'm taking this class right now and it is certainly an interesting twist on the topic - its fun to see how many different ML techniques solve variants of a single base problem that you can analyze with statistical learning theory. Also: how many different regularizations are equivalent, and how some "intuitive", ad-hoc-seem-to-work regularizations you might think up in isolation actually can be theoretically justified. It contrasts with the more traditional, also grad-level 6.867 ML class.

Part of the course requirement seems to be improving the quality of relevant Wikipedia articles: http://www.mit.edu/~9.520/fall13/projects/Projects2013.pdf

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