Acerca de este artículo

Product Identifiers

PublisherMIT Press

ISBN-10026201243X

ISBN-139780262012430

eBay Product ID (ePID)27038265898

Product Key Features

Number of Pages584 Pages

LanguageEnglish

Publication NameIntroduction to Machine Learning

SubjectMachine Theory, Computer Engineering

Publication Year2009

FeaturesNew Edition

TypeTextbook

Subject AreaComputers

AuthorEthem Alpaydin

SeriesAdaptive Computation and Machine Learning Ser.

FormatHardcover

Dimensions

Item Height1.2 in

Item Weight42.3 Oz

Item Length9.1 in

Item Width7.9 in

Additional Product Features

Edition Number2

Intended AudienceTrade

LCCN2009-013169

Dewey Edition23

Reviews"This volume offers a very accessible introduction to the field of machine learning. Ethem Alpaydin gives a comprehensive exposition of the kinds of modeling and prediction problems addressed by machine learning, as well as an overview of the most common families of paradigms, algorithms, and techniques in the field. The volume will be particularly useful to the newcomer eager to quickly get a grasp of the elements that compose this relatively new and rapidly evolving field." -Joaquin Quinonero-Candela, coeditor, Data-Set Shift in Machine Learning, "A few years ago, I used the first edition of this book as a reference book for aproject I was working on. The clarity of the writing, as well as the excellent structure and scope,impressed me. I am more than pleased to find that this second edition continues to be highlyinformative and comprehensive, as well as easy to read and follow." Radu State ComputingReviews, "A few years ago, I used the first edition of this book as a reference book for a project I was working on. The clarity of the writing, as well as the excellent structure and scope, impressed me. I am more than pleased to find that this second edition continues to be highly informative and comprehensive, as well as easy to read and follow." Radu State Computing Reviews

IllustratedYes

Dewey Decimal006.3/1

Edition DescriptionNew Edition

SynopsisA new edition of an introductory text in machine learning that gives a unified treatment of machine learning problems and solutions., 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. The second edition of 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. In order to present a unified treatment of machine learning problems and solutions, it discusses many methods from different fields, including statistics, pattern recognition, neural networks, artificial intelligence, signal processing, control, and data mining. All learning algorithms are explained so that the student can easily move from the equations in the book to a computer program. The text covers such topics as supervised learning, Bayesian decision theory, parametric methods, multivariate methods, multilayer perceptrons, local models, hidden Markov models, assessing and comparing classification algorithms, and reinforcement learning. New to the second edition are chapters on kernel machines, graphical models, and Bayesian estimation; expanded coverage of statistical tests in a chapter on design and analysis of machine learning experiments; case studies available on the Web (with downloadable results for instructors); and many additional exercises. All chapters have been revised and updated. Introduction to Machine Learning can be used by advanced undergraduates and graduate students who have completed courses in computer programming, probability, calculus, and linear algebra. It will also be of interest to engineers in the field who are concerned with the application of machine learning methods.

LC Classification NumberQ325.5.A46 2010