An introduction to machine learning

Research output: Book/ReportBook

85 Scopus citations


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.

Original languageEnglish (US)
PublisherSpringer International Publishing
Number of pages291
ISBN (Print)9783319200101, 9783319200095
StatePublished - Jan 1 2015

ASJC Scopus subject areas

  • Computer Science(all)


Dive into the research topics of 'An introduction to machine learning'. Together they form a unique fingerprint.

Cite this