Advertisement

© 2019

Applied Machine 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

Textbook

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

About this book

Introduction

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.

Keywords

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

Authors and affiliations

  1. 1.Computer Science DepartmentUniversity of Illinois Urbana ChampaignUrbanaUSA

About the authors

David Forsyth grew up in Cape Town. He received a B.Sc. (Elec. Eng.) from the University of the Witwatersrand, Johannesburg in 1984, an M.Sc. (Elec. Eng.) from that university in 1986, and a D.Phil. from Balliol College, Oxford in 1989. He spent three years on the faculty at the University of Iowa, ten years on the faculty at the University of California at Berkeley, and then moved to the University of Illinois. He served as program co-chair for IEEE Computer Vision and Pattern Recognition in 2000, 2011, 2018 and 2021; general co-chair for CVPR 2006 and ICCV 2019, and program co-chair for the European Conference on Computer Vision 2008, and is a regular member of the program committee of all major international conferences on computer vision. He has served six terms on the SIGGRAPH program committee. In 2006, he received an IEEE technical achievement award, in 2009 he was named an IEEE Fellow, and in 2014 he was named an ACM Fellow. He served as Editor-in-Chief of IEEE TPAMI from 2014-2017. He is lead co-author of Computer Vision: A Modern Approach, a textbook of computer vision that ran to two editions and four languages. He is sole author of Probability and Statistics for Computer Science, which provides the background for this book. Among a variety of odd hobbies, he is a compulsive diver, certified up to normoxic trimix level.

Bibliographic information

Industry Sectors
Automotive
Chemical Manufacturing
Biotechnology
IT & Software
Telecommunications
Law
Consumer Packaged Goods
Pharma
Materials & Steel
Finance, Business & Banking
Electronics
Energy, Utilities & Environment
Aerospace
Oil, Gas & Geosciences
Engineering