Steven W. Knox
An introduction to machine learning that includes fundamental methods; techniques; and applications.
Machine Learning: a Concise Introduction (PDF) offers a comprehensive introduction to the approaches; core concepts; and applications of machine learning. The author — an expert in the field — presents terminology; fundamental ideas; and techniques for solving applied problems in classification; clustering; regression; density estimation; and dimension reduction. The design principles behind the techniques are emphasized; including the bias-variance trade-off and its influence on the design of ensemble methods. Understanding these principles leads to more successful and flexible applications. Machine Learning: a Concise Introduction also includes methods for risk estimation; optimization; and model selection— essential elements of most applied projects.
This important resource:
- Contains useful information for effectively communicating with clients
- Presents R source code which shows how to apply and interpret many of the techniques covered
- Includes many thoughtful exercises as an integral part of the textbook; with an appendix of selected solutions
- Illustrates many classification methods with a single; running example; highlighting similarities and differences between methods
A volume in the popular Wiley Series in Probability and Statistics; Machine Learning: a Concise Introduction offers the practical information needed for an understanding of the methods and application of machine learning.
NOTE: This source only includes the ebook Machine Learning: a Concise Introduction by Knox in PDF. No access codes included.