Probabilistic Machine Learning: An Introduction
Adaptive Computation and Machine Learning Thomas Dietterich Editor Christopher Bishop David Heckerman Michael Jordan and Michael Kearns Associate Editors Bioinformatics: The Machine Learning Appronch Pierre Baldi and Soren Brunak Reinforcement Lerning: An Introduction Richard S. Sutton and Andrew G. Barto Graphical Models for Mechine Learning and Digital Communication Brendan J. Frey Learning in Grap/icel Models Michael I. Jordan Ceusation Predliction and Seorch second edition Peter Spirtes Clark Glymour and Richard Scheines Principles of Data Mining David Hand Heiki Mannila and Padhraic Smyth Bioinformatics: The Machine Learning Approch second edition Pierre Baldi and Soren Brunak Learning Kernel Classifiers: Theory and Algorithms Ralf Herbrich Leorning writh Kernels: Support Vector Machines Regularization Optimization and Beyond Bernhard Scholkopf and Alexander J. Smola Introduction to Mechine Learning Ethem Alpaydin Gaussian Processes for AMachine Learning Carl Edward Rasmussen and Christopher K.I. Williams Seri-Supereised Lerning Olivier Chapelle Bernhard Scholkopf and Alexander Zien Eds. The Minirnum Description Length Principle Peter D. Grinwald Introduction to Statistical Relational Learning Lise Getoor and Ben Taskar Eds. Probabilistic Grophical 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 Aachine Learning: A Probabilistic Perspective Kevin P. Murphy Foundations of Mac/hine Learning Mehryar Mohri Afshin Rostasmi and Ameet Talwalker
ProbabilisticMachine Learning: An Introduction Kevin P. Murphy The MIT Press Cambridge Massachusetts London England
C 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 ments 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 Congres Cataloging-in-Publication Data is available. ISBN: 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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