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Title: Introduction to Machine Learning, 3rd Edition Author: Ethem Alpaydin Length: 640 pages Edition: 3rd Language: English Publisher: The MIT Press Publication Date: 2014-08-22 ISBN-10: 0262028182 ISBN-13: 9780262028189 The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. Introduction to Machine Learning is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine learning texts. Subjects include supervised learning; Bayesian decision theory; parametric, semi-parametric, and nonparametric methods; multivariate analysis; hidden Markov models; reinforcement learning; kernel machines; graphical models; Bayesian estimation; and statistical testing. Machine learning is rapidly becoming a skill that computer science students must master before graduation. The third edition of Introduction to Machine Learning reflects this shift, with added support for beginners, including selected solutions for exercises and additional example data sets (with code available online). Other substantial changes include discussions of outlier detection; ranking algorithms for perceptrons and support vector machines; matrix decomposition and spectral methods; distance estimation; new kernel algorithms; deep learning in multilayered perceptrons; and the nonparametric approach to Bayesian methods. All learning algorithms are explained so that students can easily move from the equations in the book to a computer program. The book can be used by both advanced undergraduates and graduate students. It will also be of interest to professionals who are concerned with the application of machine learning method
Introduction to Machine earning Third Edition Adaptive Computation and Machine learning Thomas Dietterich, editor Christopher Bishop, David Heckerman, Michael Jordan, and michael Kearns, associate editors A complete list of books published in The Adaptive Computation and Machine learning series appears at the back of this book Introduction to Machine Learning Third edition Ethem alpaydin The mit Press Cambridge, massachusetts London, england c 2014 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 informa tion storage and retrieval) without permission in writing from the publisher For information about special quantity discounts, please email special_sales@mitpress. Typeset in 10/13 Lucida bright by the author using ITEX 28 Printed and bound in the united states of america Library of Congress Cataloging-in-Publication Information Alpaydin, Ethem Introduction to machine learning / Ethem Alpaydin--3rd ed p. Cm. Includes bibliographical references and index isBn 978-0-262-02818-9(hardcover: alk. paper) 1. Machine learning. I. Title Q325.5.A462014 006.3’1-dc23 2014007214 10987654321 Contents 5.11 References 113 6 Dimensionality reduction 115 6.1 Introduction 115 6.2 Subset selection 116 6.3 Principal Component Analysis 120 6.4 Feature Embedding 127 6.5 Factor Analy 6.6 Singular value Decomposition and matrix Factorization 135 6.7 Multidimensional Scaling 136 6.8 Linear Discriminant Analysis 140 6.9 Canonical Correlation Analysis 145 6.10 Isomap 148 6.11 Locally linear embedding 150 6.12 Laplacian Eigenmaps 153 6.13 Notes 155 6.14 Exercises 157 6.15 References 158 7 Clustering 161 7.1 Introduction 161 7.2 Mixture densities 162 7.3 k-Means Clustering 163 7.4 Expectation-Maximization Algorithm 167 7.5 Mixtures of latent variable models 172 7.6 Supervised Learning after clustering 173 7.7 Spectral Clustering 175 7. 8 Hierarchical Clustering 176 7.9 Choosing the Number of Clusters 178 7.10 Notes 179 7.11 Exercises 180 7.12 References 182 8 Nonparametric Methods 185 8.1 Introduction 185 8.2 Nonparametric Density Estimation 186 8.2.1 Histogram Estimator 187 8.2.2 Kernel estimator 188 8.2.3 k-Nearest Neighbor Estimator 190 8.3 Generalization to multivariate Data 192 Brief contents 1 Introduction 1 2 Supervised learning 21 3 Bayesian Decision Theory 49 4 Parametric Methods 65 5 Multivariate Methods 93 6 Dimensionality reduction 15 7 Clusteri 161 8 Nonparametric Methods 185 9 Decision trees 213 1 0 Linear Discrimination 239 11 Multilayer Perceptrons 267 12 Local models 317 1 3 Kernel machines 349 14 Graphical models 387 15 Hidden markov Models 417 16 Bayesian Estimation 445 17 Combining Multiple learners 487 18 Reinforcement Learning 517 19 Design and Analysis of machine learning Experiments 547 a Probability 593

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