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An Introduction to Machine Learning

Authors: Kubat, Miroslav

  • Offers frequent opportunities to practice techniques with control questions, exercises, thought experiments, and computer assignments.
  • Reinforces principles using well-selected toy domains and relevant real-world applications.
  • Provides additional material, including an instructor's manual with presentation slides, as well as answers to exercises.
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eBook $44.99
price for USA (gross)
  • ISBN 978-3-319-63913-0
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $59.99
price for USA
  • ISBN 978-3-319-63912-3
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
About this Textbook

This textbook presents fundamental machine learning concepts in an easy to understand manner by providing practical advice, using straightforward examples, and offering engaging discussions of relevant applications. The main topics include Bayesian classifiers, nearest-neighbor classifiers, linear and polynomial classifiers, decision trees, neural networks, and support vector machines. Later chapters show how to combine these simple tools by way of “boosting,” how to exploit them in more complicated domains, and how to deal with diverse advanced practical issues. One chapter is dedicated to the popular genetic algorithms.

This revised edition contains three entirely new chapters on critical topics regarding the pragmatic application of machine learning in industry. The chapters examine multi-label domains, unsupervised learning and its use in deep learning, and logical approaches to induction. Numerous chapters have been expanded, and the presentation of the material has been enhanced. The book contains many new exercises, numerous solved examples, thought-provoking experiments, and computer assignments for independent work.

About the authors

Miroslav Kubat, Associate Professor at the University of Miami, has been teaching and studying machine learning for over 25 years. He has published more than 100 peer-reviewed papers, co-edited two books, served on the program committees of over 60 conferences and workshops, and is an editorial board member of three scientific journals. He is widely credited with co-pioneering research in two major branches of the discipline: induction of time-varying concepts and learning from imbalanced training sets. He also contributed to research in induction from multi-label examples, induction of hierarchically organized classes, genetic algorithms, and initialization of neural networks.

Table of contents (17 chapters)

  • A Simple Machine-Learning Task

    Kubat, Miroslav

    Pages 1-18

  • Probabilities: Bayesian Classifiers

    Kubat, Miroslav

    Pages 19-41

  • Similarities: Nearest-Neighbor Classifiers

    Kubat, Miroslav

    Pages 43-64

  • Inter-Class Boundaries: Linear and Polynomial Classifiers

    Kubat, Miroslav

    Pages 65-90

  • Artificial Neural Networks

    Kubat, Miroslav

    Pages 91-111

Buy this book

eBook $44.99
price for USA (gross)
  • ISBN 978-3-319-63913-0
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $59.99
price for USA
  • ISBN 978-3-319-63912-3
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
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Bibliographic Information

Bibliographic Information
Book Title
An Introduction to Machine Learning
Authors
Copyright
2017
Publisher
Springer International Publishing
Copyright Holder
The Editor(s) (if applicable) and The Author(s) 2018
eBook ISBN
978-3-319-63913-0
DOI
10.1007/978-3-319-63913-0
Hardcover ISBN
978-3-319-63912-3
Edition Number
2
Number of Pages
XIII, 348
Number of Illustrations and Tables
82 b/w illustrations, 3 illustrations in colour
Topics