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    Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning) (Hardback) By (author) Kevin P. Murphy

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    DescriptionToday's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package--PMTK (probabilistic modeling toolkit)--that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.


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  • Full bibliographic data for Machine Learning

    Title
    Machine Learning
    Subtitle
    A Probabilistic Perspective
    Authors and contributors
    By (author) Kevin P. Murphy
    Physical properties
    Format: Hardback
    Number of pages: 1104
    Width: 204 mm
    Height: 232 mm
    Thickness: 44 mm
    Weight: 1,940 g
    Language
    English
    ISBN
    ISBN 13: 9780262018029
    ISBN 10: 0262018020
    Classifications

    BIC E4L: COM
    B&T Book Type: NF
    B&T Modifier: Region of Publication: 01
    Nielsen BookScan Product Class 3: S10.3T
    LC subject heading:
    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
    LC subject heading:
    Ingram Subject Code: XG
    Libri: I-XG
    LC subject heading:
    DC22: 006.3/1
    LC subject heading:
    DC22: 006.31
    BIC subject category V2: UYQM
    BISAC V2.8: COM037000
    DC23: 006.31
    LC classification: Q325.5 .M87 2012
    Ingram Theme: ASPT/SCITAS
    Thema V1.0: UYQM
    Edition statement
    New ed.
    Illustrations note
    300 color illus., 165 b&w illus.
    Publisher
    MIT Press Ltd
    Imprint name
    MIT Press
    Publication date
    18 September 2012
    Publication City/Country
    Cambridge, Mass.
    Author Information
    Kevin P. Murphy is a Research Scientist at Google. Previously, he was Associate Professor of Computer Science and Statistics at the University of British Columbia.
    Review quote
    This comprehensive book should be of great interest to learners and practitioners in the field of machine learning. British Computer Society