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Kevin P. Murphy - Probabilistic Machine Learning : An Introduction

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Kevin P. Murphy Probabilistic Machine Learning : An Introduction
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Contents Guide Print Page List 2022 Massachusetts Institute of Technology - photo 1

Contents
Guide
Print Page List

2022 Massachusetts Institute of Technology

This work is subject to a Creative Commons CC-BY-NC-ND license.

Picture 2

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.

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 9780262369305

Subjects: LCSH: Machine learning. | Probabilities.

Classification: LCC Q325.5 .M872 2022 | DDC 006.3/1dc23

LC record available at https://lccn.loc.gov/2021027430

10 9 8 7 6 5 4 3 2 1

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Probabilistic Machine Learning

Adaptive Computation and Machine Learning

Francis Bach, editor

Bioinformatics: The Machine Learning Approach, Pierre Baldi and Sren 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, ed.

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 Sren Brunak

Learning Kernel Classifiers: Theory and Algorithms, Ralf Herbrich

Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond, Bernhard Schlkopf and Alexander J. Smola

Introduction to Machine Learning, Ethem Alpaydn

Gaussian Processes for Machine Learning, Carl Edward Rasmussen and Christopher K.I. Williams

Semi-Supervised Learning, Olivier Chapelle, Bernhard Schlkopf, and Alexander Zien, eds.

The Minimum Description Length Principle, Peter D. Grnwald

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 Alpaydn

Machine Learning in Non-Stationary Environments: Introduction to Covariate Shift Adaptation, Masashi Sugiyama and Motoaki Kawanabe

Boosting: Foundations and Algorithms, Robert E. Schapire and Yoav Freund

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