An Introduction to Machine Learning / by Miroslav Kubat.

Format:

Book

Author/Creator:

Kubát, Miroslav, Author.

Contributor:

SpringerLink (Online service)

Series:

Computer Science (SpringerNature-11645)

Language:

English

Subjects (All):

Artificial intelligence.

Business information services.

Computer science-Mathematics.

Mathematical statistics.

Data mining.

Computer science.

Computational intelligence.

Artificial Intelligence.

IT in Business.

Probability and Statistics in Computer Science.

Data Mining and Knowledge Discovery.

Theory of Computation.

Computational Intelligence.

Local Subjects:

Artificial Intelligence.

IT in Business.

Probability and Statistics in Computer Science.

Data Mining and Knowledge Discovery.

Theory of Computation.

Computational Intelligence.

Physical Description:

1 online resource (XVIII, 458 pages) : 114 illustrations, 5 illustrations in color.

Edition:

3rd ed. 2021.

Contained In:

Springer Nature eBook

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2021.

System Details:

text file PDF

Summary:

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.

Contents:

1. Ambitions and Goals of Machine Learning

2. Probabilities: Bayesian Classifiers

3. Similarities: Nearest-Neighbor Classifiers

4. Inter-Class Boundaries: Linear and Polynomial Classifiers

5. Decision Trees

6. Artificial Neural Networks

7. Computational Learning Theory

8. Experience from Historical Applications

9. Voting Assemblies and Boosting

10. Classifiers in the Form of Rule-Sets

11. Practical Issues to Know About

12. Performance Evaluation

13. Statistical Significance

14. Induction in Multi-Label Domains

15. Unsupervised Learning

16. Deep Learning

17. Reinforcement Learning: N-Armed Bandits and Episodes

18. Reinforcement Learning: From TD(0) to Deep-Q-Learning

19. Temporal Learning

20. Hidden Markov Models

21. Genetic Algorithm

Bibliography

Index.

Other Format:

Printed edition:

ISBN:

978-3-030-81935-4

9783030819354

Access Restriction:

Restricted for use by site license.