Machine learning : a concise introduction 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. STEVEN W. KNOX holds a Ph. D. in Mathematics from the University of Illinois and an M.S. in Statistics from Carnegie Mellon University. He has over twenty years experience in using Machine Learning Statistics and Mathematics to solve real world problems. He currently serves as Technical Director of Mathematics Research and Senior Advocate for Data Science at the National Security Agency