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Machine Learning - a Concise Introduction (Hardcover)
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Machine Learning - a Concise Introduction (Hardcover)
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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.
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