I am a CS graduate student and I am starting to get really interested in Machine Learning (and Predictive Analytics). I have started working on a text classification project with a professor to learn the field but I am realizing that my background is pretty weak. The professor is too busy to teach me the basics, so I have to do it on my own.
I have done some calculus, stat 101 and linear algebra in the distant past, but I do not remember too much. But I am pretty sure if I get a good book, I can pick things up pretty quickly.
As of now I have started off my Machine Learning studies by reading Alpaydin's Machine Learning but it is pretty challenging given the big holes in my background.
I am looking for suggestions on books on major topics (Linear Algebra, Statistics, Probability, Optimization, and anything else that might be relevant) that would help me ramp up relatively quickly, given my background.
I am looking at doing it in multiple iterations. First, get what I need to do be able to do get some practical work done (like my current classification project), and then go back and read books to get deeper understanding, and so on.
Please note that at this point I am only looking at formal treatment of the subjects (textbooks only).
EDIT 1: I think I may have found stats and probability book (All of Statistics: A Concise Course in Statistical Inference by Larry Wasserman). Just need to figure out how to put the remaining pieces together. I welcome any further ideas in this regard.
Best Answer
Have you seen the Stanford online class on machine learning? It might be a great way to learn machine learning in general.
References on text mining in particular are a different question; I don't have any particular suggestions on that.