Machine learning textbook

Machine Learning: a Probabilistic Perspective

A new textbook by Kevin Murphy (in preparation, to be published by MIT Press, August 2012)

Table of contents, 9 November 2011
Promotional flyer from MIT Press (December 2011)
Chapter 1 (Introduction), 5 November 2011
Matlab software
All the figures, together with matlab code to generate them (10-Oct-2011)

My book (MLaPP) is similar to Bishop's Pattern recognition and machine learning, Hastie et al's The Elements of Statistical Learning, and to Wasserman's All of statistics, with the following key differences:

MLaPP is more accessible to undergrads. It pre-supposes a background in probability, linear algebra, calculus, and programming; however, the mathematical level ramps up slowly, with more difficult sections clearly denoted as such. This makes the book suitable for both undergrads and grads. Appendices provide summaries of the relevant mathematical background, on topics such as linear algebra, optimization and classical statistics, making the book self-contained.
MLaPP is more practically-oriented. In particular, it comes with Matlab software to reproduce almost every figure, and to implement almost every algorithm, discussed in the book. It includes many worked examples of the methods applied to real data, with readable source code online.
MLaPP covers various important topics that are not discussed in these other books, such as conditional random fields, deep learning, etc.
MLaPP is "more Bayesian" than the Hastie or Wasserman books, but "more frequentist" than the Bishop book. In particular, in MLaPP, we make extensive use of MAP estimation, which we regard as "poor man's Bayes". We prefer this to the regularization interpretation of MAP, because then all the methods in the book (except cross validation...) can be viewed as probabilistic inference, or some approximation thereof. The MAP interpretation also allows for an easy "upgrade path" to more accurate methods of approximate Bayesian inference, such as empirical Bayes, variational Bayes, MCMC, SMC, etc.
The emphasis is on simple parametric models (linear and logistic regression, discriminant analysis/ naive Bayes, mixture models, factor analysis, graphical models, etc.), which are the ones most often used in practice. However, we also briefly discuss non-parametric models, such as Gaussian processes and Dirichlet processes.