AN INTRODUCTION TO MACHINE LEARNING THAT INCLUDES THE FUNDAMENTAL TECHNIQUES, METHODS, AND APPLICATIONS

Machine Learning: a Concise Introduction offers a comprehensive introduction to the core concepts, approaches, and applications of machine learning. The author--an expert in the field--presents fundamental ideas, terminology, and techniques for solving applied problems in classification, regression, clustering, 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 flexible and successful applications. Machine Learning: a Concise Introduction also includes methods for optimization, risk estimation, and model selection--essential elements of most applied projects. This important resource:

• Illustrates many classification methods with a single, running example, highlighting similarities and differences between methods
• 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 text, with an appendix of selected solutions
• Contains useful information for effectively communicating with clients

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.