Machine learning textbook

Machine Learning: a Probabilistic Perspective

by Kevin Patrick Murphy

Cover image

  • Hardcopy available from There is only one edition of the book. However, there are multiple print runs of the hardcopy, which have fixed various errors (mostly typos). The latest printing is the fourth printing (Sep. 2013). This is what Amazon (at least in the USA) is shipping. Note: page numbering can be different between printings, although the section numbers, figure numbers, and equation numbers are the same.
  • Ecopy available from MIT Press. (The Kindle version is still (as of 4 March 2014) from the first printing, which has many errors, so do not buy the ecopy from Amazon! The MIT Press version is up to date.)
  • As of 10/19/15, a Korean version of the book is available.
  • Table of contents
  • Chapter 1 (Introduction)
  • Chapter 19 (Undirected graphical models/ Markov random fields). Note: this is from the third printing. This corrects some errors that were found (by Sebastien Bratieres) in sec 19.7.
  • Bibliography
  • Errata
  • Matlab software
  • All the figures, together with matlab code to generate them
  • My book has won the 2013 De Groot Prize for best textbook on Statistical Science.
  • Best selling machine learning book on (22 October 2012).
  • Best selling book at MIT Press (24 November 2012).
  • Resources for instructors from MIT Press. If you are an official instructor, you can request an e-copy, which can help you decide if the book is suitable for your class. You can also request the solutions manual. Slides are not available.


    Comparison to other books on the market

    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: