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Machine Learning
The Art and Science of Algorithms that Make Sense of Data

$59.99 (P)

  • Date Published: November 2012
  • availability: In stock
  • format: Paperback
  • isbn: 9781107422223

$ 59.99 (P)
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About the Authors
  • As 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.

    • Prologue and Chapter 1 are freely available online
    • Pedagogic features include boxes summarising relevant background material and a list of 'important points to remember'
    • Epilogue includes open problems in machine learning
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    Reviews & endorsements

    "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." < /br>Fernando Berzal, Computing Reviews

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    Product details

    • Date Published: November 2012
    • format: Paperback
    • isbn: 9781107422223
    • length: 409 pages
    • dimensions: 246 x 190 x 18 mm
    • weight: 0.88kg
    • contains: 120 colour illus. 15 tables
    • availability: In stock
  • 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.

  • Resources for

    Machine Learning

    Peter Flach

    General Resources

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    These resources are provided free of charge by Cambridge University Press with permission of the author of the corresponding work, but are subject to copyright. You are permitted to view, print and download these resources for your own personal use only, provided any copyright lines on the resources are not removed or altered in any way. Any other use, including but not limited to distribution of the resources in modified form, or via electronic or other media, is strictly prohibited unless you have permission from the author of the corresponding work and provided you give appropriate acknowledgement of the source.

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  • Instructors have used or reviewed this title for the following courses

    • Advanced Machine Learning
    • Autonomous Robotic Systems
    • Data Science
    • Intelligent System
    • Introduction to Machine Learning
    • Machine Learning for Computational Linguistics
    • Object Detection, Tracking and 3D Reconstruction
    • Special topic on text mining
    • Statistical Learning
    • Topics and Experiences in Electronics and Computer Technology
    • Topics in Machine Learning
  • Author

    Peter Flach, University of Bristol
    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.

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