A Concise Introduction to Machine Learning
eBook - ePub

A Concise Introduction to Machine Learning

  1. 334 pages
  2. English
  3. ePUB (mobile friendly)
  4. Available on iOS & Android
eBook - ePub

A Concise Introduction to Machine Learning

About this book

A Concise Introduction to Machine Learning uses mathematics as the common language to explain a variety of machine learning concepts from basic principles and illustrates every concept using examples in both Python and MATLAB®, which are available on GitHub and can be run from there in Binder in a web browser. Each chapter concludes with exercises to explore the content.

The emphasis of the book is on the question of Why—only if "why" an algorithm is successful is understood, can it be properly applied and the results trusted. Standard techniques are treated rigorously, including an introduction to the necessary probability theory. This book addresses the commonalities of methods, aims to give a thorough and in-depth treatment and develop intuition for the inner workings of algorithms, while remaining concise.

This useful reference should be essential on the bookshelf of anyone employing machine learning techniques, since it is born out of strong experience in university teaching and research on algorithms, while remaining approachable and readable.

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Yes, you can access A Concise Introduction to Machine Learning by A.C. Faul in PDF and/or ePUB format, as well as other popular books in Computer Science & Business Intelligence. We have over one million books available in our catalogue for you to explore.

Table of contents

  1. Cover Page
  2. Endorsements Page
  3. Half-Title Page
  4. Series Page
  5. Title Page
  6. Copyright Page
  7. Dedication Page
  8. Contents
  9. List of Figures
  10. List of Listings
  11. Foreword
  12. Preface
  13. Acknowledgments
  14. Chapter 1 Introduction
  15. Chapter 2 Probability Theory
  16. Chapter 3 Sampling
  17. Chapter 4 Linear Classification
  18. Chapter 5 Non-Linear Classification
  19. Chapter 6 Clustering
  20. Chapter 7 Dimensionality Reduction
  21. Chapter 8 Regression
  22. Chapter 9 Feature Learning
  23. Appendix A Matrix Formulae
  24. Index