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Probabilistic Machine Learning
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Probabilistic Machine Learning
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Probabilistic Machine Learning
Adaptive Computation and Machine Learning
Thomas Dietterich, Editor
Christopher Bishop, David Heckerman, Michael Jordan, and Michael Kearns, Associate Editors
Bioinformatics: The Machine Learning Approach, Pierre Baldi and Søren Brunak
Reinforcement Learning: An Introduction, Richard S. Sutton and Andrew G. Barto
Graphical Models for Machine Learning and Digital Communication, Brendan J. Frey
Learning in Graphical Models, Michael I. Jordan
Causation, Prediction, and Search, second edition, Peter Spirtes, Clark Glymour, and Richard
Scheines
Principles of Data Mining, David Hand, Heikki Mannila, and Padhraic Smyth
Bioinformatics: The Machine Learning Approach, second edition, Pierre Baldi and Søren Brunak
Learning Kernel Classifiers: Theory and Algorithms, Ralf Herbrich
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond,
Bernhard Schölkopf and Alexander J. Smola
Introduction to Machine Learning, Ethem Alpaydin
Gaussian Processes for Machine Learning, Carl Edward Rasmussen and Christopher K.I. Williams
Semi-Supervised Learning, Olivier Chapelle, Bernhard Schölkopf, and Alexander Zien, Eds.
The Minimum Description Length Principle, Peter D. Grünwald
Introduction to Statistical Relational Learning, Lise Getoor and Ben Taskar, Eds.
Probabilistic Graphical Models: Principles and Techniques, Daphne Koller and Nir Friedman
Introduction to Machine Learning, second edition, Ethem Alpaydin
Boosting: Foundations and Algorithms, Robert E. Schapire and Yoav Freund
Machine Learning: A Probabilistic Perspective, Kevin P. Murphy
Foundations of Machine Learning, Mehryar Mohri, Afshin Rostami, and Ameet Talwalker
Probabilistic Machine Learning: An Introduction, Kevin P. Murphy
Probabilistic Machine Learning
An Introduction
Kevin P. Murphy
The MIT Press
Cambridge, Massachusetts
London, England
© 2022 Massachusetts Institute of Technology
This work is subject to a Creative Commons CC-BY-NC-ND license.
Subject to such license, all rights are reserved.
The MIT Press would like to thank the anonymous peer reviewers who provided comments on drafts of this
book. The generous work of academic experts is essential for establishing the authority and quality of our
publications. We acknowledge with gratitude the contributions of these otherwise uncredited readers.
Printed and bound in the United States of America.
Library of Congress Cataloging-in-Publication Data
Names: Murphy, Kevin P., author.
Title: Probabilistic machine learning : an introduction / Kevin P. Murphy.
Description: Cambridge, Massachusetts : The MIT Press, [2022]
Series: Adaptive computation and machine learning series
Includes bibliographical references and index.
Identifiers: LCCN 2021027430 | ISBN 9780262046824 (hardcover)
Subjects: LCSH: Machine learning. | Probabilities.
Classification: LCC Q325.5 .M872 2022 | DDC 006.3/1–dc23
LC record available at https://lccn.loc.gov/2021027430
10 9 8 7 6 5 4 3 2 1
This book is dedicated to my mother, Brigid Murphy,
who introduced me to the joy of learning and teaching.
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