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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.
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"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, Dataset Shift in Machine LearningBiographie de l'auteur :
Ethem Alpaydin is Professor in the Department of Computer Engineering at Bogazici University, Istanbul.
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Description du livre The MIT Press. Paperback. Etat : New. International Edition. Very fast shipping. Receive your book in 2-7 business days if you checkout with expedited shipping. We take pride in our customer service, please contact us if you have any questions regarding the listing. N° de réf. du vendeur in-us-9780262012430
Description du livre Cumberland, Rhode Island, U.S.A.: Mit Pr, 2010. Hardcover. Etat : New. 2nd Edition. Ship out 1-2 business day,Brand new,US edition, Free tracking number usually 2-4 biz days delivery to worldwide Same shipping fee with US, Canada,Europe country, Australia, item will ship out from either LA or Asia,kf. N° de réf. du vendeur ABE-7636576149
Description du livre The MIT Press, 2009. Hardcover. Etat : New. Never used!. N° de réf. du vendeur P11026201243X