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    Machine Learning: The Art and Science of Algorithms That Make Sense of Data (Paperback) By (author) Peter Flach

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    DescriptionAs one of the most comprehensive machine learning texts around, this book does justice to the field's incredible richness, but without losing sight of the unifying principles. Peter Flach's clear, example-based approach begins by discussing how a spam filter works, which gives an immediate introduction to machine learning in action, with a minimum of technical fuss. Flach provides case studies of increasing complexity and variety with well-chosen examples and illustrations throughout. He covers a wide range of logical, geometric and statistical models and state-of-the-art topics such as matrix factorisation and ROC analysis. Particular attention is paid to the central role played by features. The use of established terminology is balanced with the introduction of new and useful concepts, and summaries of relevant background material are provided with pointers for revision if necessary. These features ensure Machine Learning will set a new standard as an introductory textbook.


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  • Full bibliographic data for Machine Learning

    Title
    Machine Learning
    Subtitle
    The Art and Science of Algorithms That Make Sense of Data
    Authors and contributors
    By (author) Peter Flach
    Physical properties
    Format: Paperback
    Number of pages: 409
    Width: 188 mm
    Height: 244 mm
    Thickness: 5 mm
    Weight: 703 g
    Language
    English
    ISBN
    ISBN 13: 9781107422223
    ISBN 10: 1107422221
    Classifications

    BIC E4L: COM
    Nielsen BookScan Product Class 3: S10.2
    B&T Book Type: NF
    B&T Modifier: Region of Publication: 03
    BIC subject category V2: UY, UT
    B&T Modifier: Academic Level: 02
    LC classification: Q
    BIC subject category V2: UMB
    B&T Modifier: Text Format: 06
    B&T General Subject: 229
    B&T Modifier: Text Format: 01
    BIC subject category V2: UYS, UYQP
    B&T Merchandise Category: COM
    BIC subject category V2: TJ
    Warengruppen-Systematik des deutschen Buchhandels: 26320
    Ingram Subject Code: XG
    Libri: I-XG
    LC subject heading:
    Abridged Dewey: 006
    DC22: 006.31
    BIC subject category V2: UYQM
    BISAC V2.8: COM016000
    DC23: 006.31
    LC subject heading:
    Ingram Theme: ASPT/SCITAS
    Thema V1.0: UYQM, UMB
    Edition statement
    New ed.
    Illustrations note
    120 colour illus. 15 tables
    Publisher
    CAMBRIDGE UNIVERSITY PRESS
    Imprint name
    CAMBRIDGE UNIVERSITY PRESS
    Publication date
    12 November 2012
    Publication City/Country
    Cambridge
    Author Information
    Peter Flach has more than twenty years of experience in machine learning teaching and research. He is Editor-in-Chief of Machine Learning and Program Co-Chair of the 2009 ACM Conference on Knowledge Discovery and Data Mining and the 2012 European Conference on Machine Learning and Data Mining. His research spans all aspects of machine learning, from knowledge representation and the use of logic to learn from highly structured data to the analysis and evaluation of machine learning models and methods to large-scale data mining. He is particularly known for his innovative use of Receiver Operating Characteristic (ROC) analysis for understanding and improving machine learning methods. These innovations have proved their effectiveness in a number of invited talks and tutorials and now form the backbone of this book.
    Review quote
    "This textbook is clearly written and well organized. Starting from the basics, the author skillfully guides the reader through his learning process by providing useful facts and insight into the behavior of several machine learning techniques, as well as the high-level pseudocode of many key algorithms." < Fernando Berzal, Computing Reviews
    Table of contents
    Prologue: a machine learning sampler; 1. The ingredients of machine learning; 2. Binary classification and related tasks; 3. Beyond binary classification; 4. Concept learning; 5. Tree models; 6. Rule models; 7. Linear models; 8. Distance-based models; 9. Probabilistic models; 10. Features; 11. In brief: model ensembles; 12. In brief: machine learning experiments; Epilogue: where to go from here; Important points to remember; Bibliography; Index.