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    Introduction to Machine Learning (Adaptive Computation and Machine Learning) (Hardback) By (author) Ethem Alpaydin

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    DescriptionThe 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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  • Full bibliographic data for Introduction to Machine Learning

    Title
    Introduction to Machine Learning
    Authors and contributors
    By (author) Ethem Alpaydin
    Physical properties
    Format: Hardback
    Number of pages: 584
    Width: 210 mm
    Height: 232 mm
    Thickness: 30 mm
    Weight: 1,220 g
    Language
    English
    ISBN
    ISBN 13: 9780262012430
    ISBN 10: 026201243X
    Classifications

    BIC E4L: COM
    B&T Book Type: NF
    B&T Modifier: Region of Publication: 01
    Nielsen BookScan Product Class 3: S10.3T
    Warengruppen-Systematik des deutschen Buchhandels: 16320
    B&T Modifier: Academic Level: 02
    B&T Modifier: Text Format: 06
    B&T General Subject: 229
    B&T Modifier: Text Format: 01
    B&T Merchandise Category: COM
    Ingram Subject Code: XG
    LC subject heading:
    BISAC V2.8: COM059000
    DC22: 006.3/1
    LC subject heading:
    DC22: 006.31
    BIC subject category V2: UYQM
    BISAC V2.8: COM037000
    LC classification: Q325.5 .A46 2010
    Thema V1.0: UYQM
    Edition
    2, Revised
    Edition statement
    2nd Revised edition
    Illustrations note
    172 figures, 10 tables
    Publisher
    MIT Press Ltd
    Imprint name
    MIT Press
    Publication date
    26 February 2010
    Publication City/Country
    Cambridge, Mass.
    Author Information
    Ethem Alpaydin is a Professor in the Department of Computer Engineering at Bogazici University, Istanbul.
    Review quote
    "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