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Pro Git - Table of Contents
Online book. (Most projects I find interesting nowadays seem to be using Git. Damn you, Linus Torvalds!)
Probabilistic Graphical Models - The MIT Press
D. Koller and N. Friedman's book. Amazingly, at US$ 95,00 it is still one of the more affordable books on this subject (compare price and number of pages with Lauritzen's or Wainwright and Jordan's)
http://www.stanford.edu/~montanar/BOOK/book.html
It should be an introduction to a rich and rapidly evolving research field at the interface between statistical physics, theretical computer science/discrete mathematics, and coding/information theory. It should be accessible to graduate students an researchers without specific training in any of these three fields.
Powell's Books - Principles and Theory for Data Mining and Machine Learning (Springer Series in Statistics) by Bertrand Clarke
Intended primarily as a graduate level textbook for statistics, computer science, and electrical engineering students, this book assumes only a strong foundation in undergraduate statistics and mathematics, and facility with using R packages.
Rcode
R code for some books (nonlinear models, basic statistics, etc)
Computer Vision: Algorithms and Applications
I thought I had bookmarked it already: Richard Szeliski's (of Photosynth fame) CV book. Thanks, thsant!
Dataset Shift in Machine Learning - The MIT Press
Dataset shift is a common problem in predictive modeling that occurs when the joint distribution of inputs and outputs differs between training and test stages.
Bulmer (1966) Principles of Statistics « LingPipe Blog
"This book is fantastic. It’s concise (250 page), incredibly clear and straightforward, and at just the right level of mathematical rigor not to get bogged down in extraneous details."
Common Java Cookbook
Might be useful if I ever need to program in Java again (very likely)
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