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.