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内容概要:本书深入讲解了机器学习的概率观点,并涵盖了多种类型的模型与算法,详细探讨了如贝叶斯网络、高斯混合模型及其相关推理算法,针对监督和非监督学习等多个方面提供了一个综合性的指导;并且提供了对各种模型的学习、使用的算法以及它们如何解决现实生活问题进行了一一阐述。书中还包括许多实例演示并讨论了如何用概率论来理解和解释机器学习。 适合人群:主要面向有一定统计和编程背景的数据科学家、研究工作者或者研究生及以上阶段的学习者。 使用场景及目标:这本书适用于那些正在从事或者将要进入数据分析领域的人士,能够帮助他们理解高级模型的内部运作机制,以及提高利用复杂的数据集解决问题的能力。 其他说明:对于希望进一步研究深度理解机器学习内在逻辑,而不是仅仅停留在表面的应用上的人员来说是一本非常好的教材及参考资料。
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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.
Machine Learning: A Probabilistic Perspective
Machine Learning
A Probabilistic Perspective
Kevin P. Murphy
The MIT Press
Cambridge, Massachusetts
London, England
© 2012 Massachusetts Institute of Technology
All rights reserved. No part of this book may be reproduced in any form by any electronic or mechanical
means (including photocopying, recording, or information storage and retrieval) without permission in
writing from the publisher.
For information about special quantity discounts, please email special_sales@mitpress.mit.edu
This book was set in the L
A
T
E
X programming language by the author. Printed and bound in the United
States of America.
Library of Congress Cataloging-in-Publication Information
Murphy, Kevin P.
Machine learning : a probabilistic perspective / Kevin P. Murphy.
p. cm. — (Adaptive computation and machine learning series)
Includes bibliographical references and index.
ISBN 978-0-262-01802-9 (hardcover : alk. paper)
1. Machine learning. 2. Probabilities. I. Title.
Q325.5.M87 2012
006.3’1—dc23
2012004558
10987654321
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