**AN INTRODUCTION TO MACHINE LEARNING THAT INCLUDES THE FUNDAMENTAL
TECHNIQUES, METHODS, AND APPLICATIONS
PROSE Award Finalist 2019
Association of American Publishers Award for Professional and Scholarly
Excellence**
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.
