An introduction to machine learning / Miroslav Kubat.

Unlimited

Authorized users

Can lend chapters, not whole ebooks

An introduction to machine learning / Miroslav Kubat.

Third edition.

9783030819354 (electronic bk.)
3030819353 (electronic bk.)
9783030819347
3030819345

Cham : Springer, [2021]

©2021

English

1 online resource : illustrations (some color)

10.1007/978-3-030-81935-4 doi

Q325.5 .K83 2021

006.3

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.

Includes bibliographical references and index.

Access limited to authorized users.

Online resource; title from PDF title page (SpringerLink, viewed October 6, 2021).

Online Resources > Ebooks
All Resources