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Introduction to Machine Learning 3ed

by Ethem Alpaydin The MIT Press
Pub Date:
Hbk 640 pages
AU$129.00 NZ$133.91
Product Status: In Stock Now
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The goal of machine learning is to program computers to use example data or pastexperience 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 behaviorso that a task can be completed using minimum resources, and extract knowledge from bioinformaticsdata.

Introduction to Machine Learning is a comprehensive textbook on thesubject, covering a broad array of topics not usually included in introductory machine learningtexts. 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 statisticaltesting. Machine learning is rapidly becoming a skill that computer sciencestudents must master before graduation. The third edition of Introduction to MachineLearning reflects this shift, with added support for beginners, including selectedsolutions for exercises and additional example data sets (with code available online). Othersubstantial changes include discussions of outlier detection; ranking algorithms for perceptrons andsupport vector machines; matrix decomposition and spectral methods; distance estimation; new kernelalgorithms; deep learning in multilayered perceptrons; and the nonparametric approach to Bayesianmethods. All learning algorithms are explained so that students can easily move from the equationsin the book to a computer program. The book can be used by both advanced undergraduates and graduatestudents. It will also be of interest to professionals who are concerned with the application ofmachine learning methods.
Ethem Alpaydin's Introduction to Machine Learning provides a niceblending of the topical coverage of machine learning (A la Tom Mitchell) with formal probabilisticfoundations (A la Christopher Bishop). This newly updated version now introduces some of the mostrecent and important topics in machine learning (e.g., spectral methods, deep learning, and learningto rank) to students and researchers of this critically important and expanding field. John W. Sheppard, Professor of Computer Science, Montana State University I have used Introduction to Machine Learning for several years inmy graduate Machine Learning course. The book provides an ideal balance of theory and practice, andwith this third edition, extends coverage to many new state-of-the-art algorithms. I look forward tousing this edition in my next Machine Learning course. Larry Holder, Professor of Electrical Engineering and Computer Science, Washington StateUniversity This volume is both a complete and accessible introduction to the machine learningworld. This is a 'Swiss Army knife' book for this rapidly evolving subject. Although intended as anintroduction, it will be useful not only for students but for any professional looking for acomprehensive book in this field. Newcomers will find clearly explained concepts and experts willfind a source for new references and ideas. Hilario GA³mez-Moreno, IEEE Senior Member, University of AlcalA¡, Spain
Ethem Alpaydin is a Professor in the Department of Computer Engineering at BogaziA§iUniversity, Istanbul.