Week 3 of the Stanford AI class was
about machine learning. This was kind of handy for me, since my
PhD thesis was mostly about
applying some unsupervised learning techniques to dimensionality
reduction for climate model output. That mean that I've read hundreds
of papers about this stuff so the basic ideas and even quite a few of
the details are already pretty familiar. However, most of what I've
read has been aimed at applications in climate science and dynamical
systems theory. Not much from the huge literature on clustering
methods, for instance.
Sebastian very briefly mentioned some of the nonlinear dimensionality
reduction methods that have been developed for unsupervised learning
applications, but it was nothing more than a mention (no time for
anything else). I can't resist the temptation to dust off a little of
this stuff from the archives.