Machine Learning
A Probabilistic Perspective
Kevin P. Murphy
“An astonishing machine learning book: intuitive, full
of examples, fun to read but still comprehensive,
strong, and deep! A great starting point for any univer-
sity student—and a must-have for anybody in the field.”
Jan Peters, Darmstadt University of Technology;
Max-Planck Institute for Intelligent Systems
“Kevin Murphy excels at unraveling 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.”
John Winn, Microsoft Research
“This is a wonderful book that starts with basic topics
in statistical modeling, culminating in the most ad-
vanced topics. It provides both the theoretical foun-
dations of probabilistic machine learning as well as
practical tools, in the form of MATLAB code. The book
should be on the shelf of any student interested in the
topic, and any practitioner working in the field.”
Yoram Singer, Google Research
“This book will be an essential reference for practitio-
ners of modern machine learning. It covers the basic
concepts needed to understand the field as a whole,
and the powerful modern methods that build on those
concepts. In Machine Learning, the language of prob-
ability and statistics reveals important connections be-
tween seemingly disparate algorithms and strategies.
Thus, its readers will become articulate in a holistic
view of the state-of-the-art and poised to build the next
generation of machine learning algorithms.”
David Blei, Princeton University
machine learning
Machine Learning
A Probabilistic Perspective
Kevin P. Murphy
Today’s Web-enabled deluge of electronic data calls for
automated methods of data analysis. Machine learning
provides these, developing methods that can automatically
detect patterns in data and use the uncovered patterns to
predict future data. This textbook offers a comprehensive
and self-contained introduction to the field of machine
learning, a unified, probabilistic approach.
The coverage combines breadth and depth, offering
necessary background material on such topics as probabili-
ty, optimization, and linear algebra as well as discussion of
recent developments in the field, including conditional ran-
dom fields, L1 regularization, and deep learning. The book
is written in an informal, accessible style, complete with
pseudo-code for the most important algorithms. All topics
are copiously illustrated with color images and worked
examples drawn from such application domains as biology,
text processing, computer vision, and robotics. Rather than
providing a cookbook of different heuristic methods, the
book stresses a principled model-based approach, often
using the language of graphical models to specify mod-
els in a concise and intuitive way. Almost all the models
described have been implemented in a MATLAB software
package—PMTK (probabilistic modeling toolkit)—that is
freely available online. The book is suitable for upper-level
undergraduates with an introductory-level college math
background and beginning graduate students.
Kevin P. Murphy is a Research Scientist at Google. Previ-
ously, he was Associate Professor of Computer Science
and Statistics at the University of British Columbia.
Adaptive Computation and Machine Learning series
The MIT Press
Massachusetts Institute of Technology
Cambridge, Massachusetts 02142
http://mitpress.mit.edu
978-0-262-01802-9
The cover image is based on sequential Bayesian updating
of a 2D Gaussian distribution. See Figure 7.11 for details.
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