Machine learning : an algorithmic perspective / by Stephen Marsland.

Format:

Book

Author/Creator:

Marsland, Stephen, eauthor.

Series:

Chapman & Hall/CRC machine learning & pattern recognition series.

Chapman & Hall/CRC machine learning & pattern recognition series

Language:

English

Subjects (All):

Machine learning.

Algorithms.

Physical Description:

1 online resource (xx, 430 pages) : illustrations.

Edition:

Second edition.

Place of Publication:

Boca Raton, FL : Chapman and Hall/CRC, an imprint of Taylor and Francis, 2014.

System Details:

text file

Summary:

A Proven, Hands-On Approach for Students without a Strong Statistical Foundation Since the best-selling first edition was published, there have been several prominent developments in the field of machine learning, including the increasing work on the statistical interpretations of machine learning algorithms. Unfortunately, computer science students without a strong statistical background often find it hard to get started in this area.

Contents:

Chapter 1. Introduction

Chapter 2. Preliminaries

Chapter 3. Neurons, neural networks, and linear discriminants

Chapter 4. The multi-layer perceptron

Chapter 5. Radial basis functions and splines

Chapter 6. Dimensionality reduction

Chapter 7. Probabilistic learning

Chapter 8. Support vector machines

Chapter 9. Optimisation and search

Chapter 10. Evolutionary learning

Chapter 11. Reinforcement learning

Chapter 12. Learning with trees

Chapter 13. Decision by committee: ensemble learning

Chapter 14. Unsupervised learning

Chapter 15. Markov Chain Monte Carlo (MCMC) methods

Chapter 16. Graphical models

Chapter 17. Symmetric weights and deep belief networks

Chapter 18. Gaussian processes

Appendix A: .Python.

Notes:

"A Chapman & Hall book."

Includes bibliographical references and index.

Description based on print version record.

ISBN:

9780429102509

042910250X

9781498759786

1498759785

OCLC:

895686170