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An Introduction to Machine Learning

Authors: Kubat, Miroslav

  • Offers a comprehensive introduction to the foundations of machine learning in a very easy-to-understand manner
  • In addition to describing techniques and algorithms, each tool is applied to their appropriate situations
  • Teaching resources include a Solutions Manual to end-of-chapter exercises, with presentation slides
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Buy this book

eBook 50,28 €
price for Spain (gross)
  • Due: November 7, 2021
  • ISBN 978-3-030-81935-4
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
Hardcover 62,39 €
price for Spain (gross)
  • Due: November 7, 2021
  • ISBN 978-3-030-81934-7
  • Free shipping for individuals worldwide
  • Institutional customers should get in touch with their account manager
  • Covid-19 shipping restrictions
  • The final prices may differ from the prices shown due to specifics of VAT rules
About this Textbook

This textbook offers a comprehensive introduction to Machine Learning techniques and algorithms. This Third Edition covers newer approaches that have become highly topical, including deep learning, and auto-encoding, introductory information about temporal learning and hidden Markov models, and a much more detailed treatment of reinforcement learning. The book is written in an easy-to-understand manner with many examples and pictures, and with a lot of practical advice and discussions of simple applications. 

The main topics include Bayesian classifiers, nearest-neighbor classifiers, linear and polynomial classifiers, decision trees, rule-induction programs, artificial neural networks, support vector machines, boosting algorithms, unsupervised learning (including Kohonen networks and auto-encoding), deep learning, reinforcement learning, temporal learning (including long short-term memory), hidden Markov models, and the genetic algorithm. Special attention is devoted to performance evaluation, statistical assessment, and to many practical issues ranging from feature selection and feature construction to bias, context, multi-label domains, and the problem of imbalanced classes.

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. Professor Kubat is also known for his many practical applications of machine learning, ranging from oil-spill detection in radar images to text categorization to tumor segmentation in MR images.

Table of contents (21 chapters)

Table of contents (21 chapters)
  • Ambitions and Goals of Machine Learning

    Pages 1-15

    Kubat, Miroslav

  • Probabilities: Bayesian Classifiers

    Pages 17-39

    Kubat, Miroslav

  • Similarities: Nearest-Neighbor Classifiers

    Pages 41-64

    Kubat, Miroslav

  • Inter-Class Boundaries: Linear and Polynomial Classifiers

    Pages 65-90

    Kubat, Miroslav

  • Decision Trees

    Pages 91-115

    Kubat, Miroslav

Buy this book

eBook 50,28 €
price for Spain (gross)
  • Due: November 7, 2021
  • ISBN 978-3-030-81935-4
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
Hardcover 62,39 €
price for Spain (gross)
  • Due: November 7, 2021
  • ISBN 978-3-030-81934-7
  • Free shipping for individuals worldwide
  • Institutional customers should get in touch with their account manager
  • Covid-19 shipping restrictions
  • The final prices may differ from the prices shown due to specifics of VAT rules
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Bibliographic Information

Bibliographic Information
Book Title
An Introduction to Machine Learning
Authors
Copyright
2021
Publisher
Springer International Publishing
Copyright Holder
Springer Nature Switzerland AG
eBook ISBN
978-3-030-81935-4
DOI
10.1007/978-3-030-81935-4
Hardcover ISBN
978-3-030-81934-7
Edition Number
3
Number of Pages
XVIII, 454
Number of Illustrations
109 b/w illustrations, 5 illustrations in colour
Topics