Machine Learning: a Concise Introduction (Wiley Series in Probability and Statistics) - Hardcover

Knox, Steven W.

Hardcover

ISBN 10: 1119439191 ISBN 13: 9781119439196

Publisher: Wiley, 2018

Synopsis

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.

About this title

About the Author

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.

From the Back Cover

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.

From the Inside Flap

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.

Edition details

• Publisher: Wiley
• Publication date: 2018
• Language: English
• ISBN 10: 1119439191
• ISBN 13: 9781119439196
• Binding: Hardcover
• Edition number: 1
• Number of pages: 346