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

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    Item specifics

    Condition
    Good: A book that has been read but is in good condition. Very minimal damage to the cover including ...
    Release Year
    2010
    Book Title
    Introduction to Machine Learning (Adaptive Computation and Mac...
    ISBN
    9780262012430
    Category

    About this product

    Product Identifiers

    Publisher
    MIT Press
    ISBN-10
    026201243X
    ISBN-13
    9780262012430
    eBay Product ID (ePID)
    27038265898

    Product Key Features

    Number of Pages
    584 Pages
    Language
    English
    Publication Name
    Introduction to Machine Learning
    Subject
    Machine Theory, Computer Engineering
    Publication Year
    2009
    Features
    New Edition
    Type
    Textbook
    Subject Area
    Computers
    Author
    Ethem Alpaydin
    Series
    Adaptive Computation and Machine Learning Ser.
    Format
    Hardcover

    Dimensions

    Item Height
    1.2 in
    Item Weight
    42.3 Oz
    Item Length
    9.1 in
    Item Width
    7.9 in

    Additional Product Features

    Edition Number
    2
    Intended Audience
    Trade
    LCCN
    2009-013169
    Dewey Edition
    23
    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
    Illustrated
    Yes
    Dewey Decimal
    006.3/1
    Edition Description
    New Edition
    Synopsis
    A 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 Number
    Q325.5.A46 2010

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