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Introduction to Machine Learning (Adaptive Computation and Machine Learning)

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Introduction to Machine Learning (Adaptive Computation and Machine Learning) Cover

 

Synopses & Reviews

Publisher Comments:

andlt;Pandgt;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.andlt;/Pandgt;

Synopsis:

A new edition of an introductory text in machine learning that gives a unified treatment of machine learning problems and solutions.

Synopsis:

andlt;Pandgt;A new edition of an introductory text in machine learning that gives a unified treatment of machine learning problems and solutions.andlt;/Pandgt;

Synopsis:

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.

About the Author

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.

Product Details

ISBN:
9780262012430
Author:
Alpaydin, Ethem
Publisher:
The MIT Press
Author:
Massachusetts Institute of Technology
Location:
Cambridge
Subject:
Machine learning
Subject:
Machine Theory
Subject:
Computer Engineering
Subject:
Computers-Reference - General
Copyright:
Edition Description:
second edition
Series:
Adaptive Computation and Machine Learning series Introduction to Machine Learning
Publication Date:
20091204
Binding:
Hardback
Grade Level:
Professional and scholarly
Language:
English
Illustrations:
172 figures, 10 tables
Pages:
584
Dimensions:
9 x 8 x 0.96 in
Age Level:
from 18

Related Subjects

Computers and Internet » Artificial Intelligence » General
Computers and Internet » Computers Reference » General
Engineering » Engineering » General Engineering
History and Social Science » Economics » General
History and Social Science » Politics » General

Introduction to Machine Learning (Adaptive Computation and Machine Learning) New Hardcover
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$63.75 In Stock
Product details 584 pages MIT Press (MA) - English 9780262012430 Reviews:
"Synopsis" by , A new edition of an introductory text in machine learning that gives a unified treatment of machine learning problems and solutions.
"Synopsis" by , andlt;Pandgt;A new edition of an introductory text in machine learning that gives a unified treatment of machine learning problems and solutions.andlt;/Pandgt;
"Synopsis" by , 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.
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