Applied Machine Learning

  • David Forsyth

Table of contents

  1. Front Matter
    Pages i-xxi
  2. Classification

    1. Front Matter
      Pages 1-1
    2. David Forsyth
      Pages 3-19
    3. David Forsyth
      Pages 21-48
    4. David Forsyth
      Pages 49-65
  3. High Dimensional Data

    1. Front Matter
      Pages 67-67
    2. David Forsyth
      Pages 69-91
    3. David Forsyth
      Pages 93-115
    4. David Forsyth
      Pages 117-138
    5. David Forsyth
      Pages 139-151
  4. Clustering

    1. Front Matter
      Pages 153-153
    2. David Forsyth
      Pages 155-182
    3. David Forsyth
      Pages 183-202
  5. Regression

    1. Front Matter
      Pages 203-203
    2. David Forsyth
      Pages 205-244
    3. David Forsyth
      Pages 245-274
    4. David Forsyth
      Pages 275-302
  6. Graphical Models

    1. Front Matter
      Pages 303-303
    2. David Forsyth
      Pages 305-332
    3. David Forsyth
      Pages 333-350
    4. David Forsyth
      Pages 351-364
  7. Deep Networks

    1. Front Matter
      Pages 365-365
    2. David Forsyth
      Pages 367-398
    3. David Forsyth
      Pages 399-421
    4. David Forsyth
      Pages 423-453
    5. David Forsyth
      Pages 455-478
  8. Back Matter
    Pages 479-494

About this book


Machine learning methods are now an important tool for scientists, researchers, engineers and students in a wide range of areas.  This book is written for people who want to adopt and use the main tools of machine learning, but aren’t necessarily going to want to be machine learning researchers. Intended for students in final year undergraduate or first year graduate computer science programs in machine learning, this textbook is a machine learning toolkit. Applied Machine Learning covers many topics for people who want to use machine learning processes to get things done, with a strong emphasis on using existing tools and packages, rather than writing one’s own code.

A companion to the author's Probability and Statistics for Computer Science, this book picks up where the earlier book left off (but also supplies a summary of probability that the reader can use).

  • Emphasizing the usefulness of standard machinery from applied statistics, this textbook gives an overview of the major applied areas in learning
  • Covers the ideas in machine learning that everyone going to use learning tools should know, whatever their chosen specialty or career.
  • Broad coverage of the area ensures enough to get the reader started, and to realize that it’s worth knowing more in-depth knowledge of the topic.
  • Practical approach emphasizes using existing tools and packages quickly, with enough pragmatic material on deep networks to get the learner started without needing to study other material.


machine learning naive bayes nearest neighbor SVM OCA PSCS linear regression Markov chains generalized linear models model selection EM structure learning

Authors and affiliations

  • David Forsyth
    • 1
  1. 1.Computer Science DepartmentUniversity of Illinois Urbana ChampaignUrbanaUSA

Bibliographic information

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