Machine Learning

a Concise Introduction

Nonfiction, Computers, Database Management
Cover of the book Machine Learning by Steven W. Knox, Wiley
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Author: Steven W. Knox ISBN: 9781119438984
Publisher: Wiley Publication: March 15, 2018
Imprint: Wiley Language: English
Author: Steven W. Knox
ISBN: 9781119438984
Publisher: Wiley
Publication: March 15, 2018
Imprint: Wiley
Language: English

**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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

**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:

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.

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