9781107422223

Machine Learning

by
  • ISBN13:

    9781107422223

  • ISBN10:

    1107422221

  • Format: Paperback
  • Copyright: 2012-11-12
  • Publisher: Cambridge Univ Pr

Note: Supplemental materials are not guaranteed with Rental or Used book purchases.

Purchase Benefits

  • Free Shipping On Orders Over $35!
    Your order must be $35 or more to qualify for free economy shipping. Bulk sales, PO's, Marketplace items, eBooks and apparel do not qualify for this offer.
  • Get Rewarded for Ordering Your Textbooks! Enroll Now
List Price: $59.99 Save up to $9.00
  • Rent Book $50.99
    Add to Cart Free Shipping

    TERM
    PRICE
    DUE
    USUALLY SHIPS IN 2-3 BUSINESS DAYS

Supplemental Materials

What is included with this book?

  • The New copy of this book will include any supplemental materials advertised. Please check the title of the book to determine if it should include any access cards, study guides, lab manuals, CDs, etc.
  • The Rental copy of this book is not guaranteed to include any supplemental materials. Typically, only the book itself is included. This is true even if the title states it includes any access cards, study guides, lab manuals, CDs, etc.

Summary

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.

Table of Contents

Prologue: a machine learning sampler
The ingredients of machine learning
Binary classification and related tasks
Beyond binary classification
Concept learning
Tree models
Rule models
Linear models
Distance-based models
Probabilistic models
Features
In brief: model ensembles
In brief: machine learning experiments
Epilogue: where to go from here
Important points to remember
Bibliography
Index
Table of Contents provided by Publisher. All Rights Reserved.

Rewards Program

Write a Review