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