Overview
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
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Table of contents (14 chapters)
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Front Matter
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Back Matter
Keywords
- Applications
- bayesian classifiers
- boosting
- computational learning theory
- decision trees
- genetic algorithms
- linear and polynomial classifiers
- nearest neighbor classifiers
- neural networks
- performance evaluation
- reinforcement learning
- statistical significance
- time-varying classes, imbalanced representation
About this book
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.
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)
Authors and Affiliations
About the author
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.
Bibliographic Information
Book Title: An Introduction to Machine Learning
Authors: Miroslav Kubat
DOI: https://doi.org/10.1007/978-3-319-20010-1
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer International Publishing Switzerland 2015
Softcover ISBN: 978-3-319-34886-5Published: 15 October 2016
eBook ISBN: 978-3-319-20010-1Published: 15 July 2015
Edition Number: 1
Number of Pages: XIII, 291
Number of Illustrations: 69 b/w illustrations, 2 illustrations in colour
Topics: Artificial Intelligence, Simulation and Modeling, Information Storage and Retrieval, Pattern Recognition