Connectionists: new book: "Machine learning: a probabilistic perspective"

[Kevin Murphy]

I am pleased to announce the publication of my book,  "Machine
learning: a probabilistic perspective" (MIT Press 2012).
This book provides a unified view of machine learning, based on
probabilistic inference and graphical models.
It is designed to be accessible to upper level undergraduates as well
as beginning graduate students. In addition, it covers various
important topics that are not in other ML textbooks, such as
conditional random fields, convex and non-convex sparsity promoting
priors, and deep learning.  Further details can be found at

   http://www.cs.ubc.ca/~murphyk/MLbook/index.html

Some endorsements:

"An astonishing machine learning book: intuitive, full of examples,
fun to read but still comprehensive, strong and deep! A great starting
point for any university student -- and a must have for anybody in the
field." -- Prof. Jan Peters, Darmstadt University of Technology/
Max-Planck Institute for Intelligent Systems

"An amazingly comprehensive survey of the field, covering both the
basic theory as well as cutting edge research. Richly illustrated and
loaded with examples and exercises. I will tell my students (and
myself) to read this cover to cover!" -- Prof. Max Welling, U.C.
Irvine

"Prof. Murphy excels at unravelling the complexities of machine
learning methods while motivating the reader with a stream of
illustrated examples and real world case studies. The accompanying
software package includes source code for many of the figures, making
it both easy and very tempting to dive in and explore these methods
for yourself. A must-buy for anyone interested in machine learning or
curious about how to extract useful knowledge from big data." -- Dr
John Winn, Microsoft Research.

"This book does a really nice job explaining the basic principles and
methods of machine learning from a Bayesian perspective. It will prove
useful to statisticians interested in the current frontiers of machine
learning as well as machine learners seeking a probabilistic
foundation for their methods. It hits the 4 c's: clear, current,
concise, and comprehensive, and it deserves a place alongside 'All of
Statistics' and 'The Elements of Machine Learning' on the practical
statistician's bookshelf." -- Dr Steven Scott, Google Quantitative
Analysis Team.