An Introduction to Machine Learning

Authors: Kubat, Miroslav

Free Preview
  • Supplies frequent opportunities to practice techniques at the end of each chapter with control questions, exercises, thought experiments, and computer assignments
  • Reinforces principles using well-selected toy domains and interesting real-world applications
  • Supplementary material will be provided including an instructor's manual with PowerPoint slides
see more benefits

Buy this book

eBook £31.99
price for United Kingdom (gross)
  • ISBN 978-3-319-20010-1
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Softcover £40.99
price for United Kingdom (gross)
  • ISBN 978-3-319-34886-5
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
About this Textbook

This book presents basic ideas of machine learning in a way that is easy to understand, by providing hands-on practical advice, using simple examples, and motivating students with discussions of interesting 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.

About the authors

Miroslav Kubat, Associate Professor at the University of Miami, has been teaching and studying machine learning for more than a quarter century. Over the years, he has published more than 100 peer-reviewed papers, co-edited two books, served on the program committees of some 60 program conferences and workshops, and is the member of the editorial boards of three scientific journals. He is widely credited for having co-pioneered research in two major branches of the discipline: induction of time-varying concepts and learning from imbalanced training sets. Apart from that, he contributed to induction from multi-label examples, induction of hierarchically organized classes, genetic algorithms, initialization of neural networks, and other problems.

Reviews

“Miroslav Kubat's Introduction to Machine Learning is an excellent overview of a broad range of Machine Learning (ML) techniques. It fills a longstanding need for texts that cover the middle ground of neither oversimplifying nor too technical explanations of key concepts of key Machine Learning algorithms. … All in all it is a very informative and instructive read which is well suited for undergraduate students and aspiring data scientists.” (Holger K. von Joua, Google+, plus.google.com, December, 2016)

“It is superbly organized: each section includes a ‘what have you learned’ summary, and every chapter has a short summary, accompanying (brief) historical remarks, and a slew of exercises. … In most of the chapters, there are very clear examples, well chosen and illustrated, that really help the reader understand each concept. … I did learn quite a bit about very basic machine learning by reading this book.” (Jacques Carette, Computing Reviews, January, 2016)


Table of contents (14 chapters)

Table of contents (14 chapters)

Buy this book

eBook £31.99
price for United Kingdom (gross)
  • ISBN 978-3-319-20010-1
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Softcover £40.99
price for United Kingdom (gross)
  • ISBN 978-3-319-34886-5
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Loading...

Recommended for you

Loading...

Bibliographic Information

Bibliographic Information
Book Title
An Introduction to Machine Learning
Authors
Copyright
2015
Publisher
Springer International Publishing
Copyright Holder
Springer International Publishing Switzerland
eBook ISBN
978-3-319-20010-1
DOI
10.1007/978-3-319-20010-1
Softcover ISBN
978-3-319-34886-5
Edition Number
1
Number of Pages
XIII, 291
Number of Illustrations
69 b/w illustrations, 2 illustrations in colour
Topics