[mimighost]

I am currently in this course.

Honestly, this is not a course that I would recommend. The most problematic part of this course is its lack of clear outline. It jumps between different fields of machine learning, which could have fundamentally different focuses and motivations, without illustrating the connections to the students. It talks about Watson-Nadaraya classifier in the second class, then we have two lectures to explain most basic naive bayes algorithm. I just don't get it.

Though it gets me confusing a lot of times, the course is useful in a way that gives me a lot of keywords to search for and read article about.Also the homeworks might be challenging some time, working through them did improve my understanding of something I might think trival before, like the linear regression stuff.

And if you are really interested, I would recommend a book, which covered most of the materials of the course while being much more organized:

http://www-stat.stanford.edu/~tibs/ElemStatLearn/

A refresher in linear algebra will also help~