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New edition of a PROSE award finalist title on core concepts for machine learning, updated with the latest developments in the field, now with Python and R source code side-by-side Machine Learning is a comprehensive text on the core concepts, approaches, and applications of machine learning. It presents fundamental ideas, terminology, and techniques for solving applied problems in classification, regression, clustering, density estimation, and dimension reduction. New content for this edition includes chapter expansions which provide further computational and algorithmic insights to improve reader understanding. This edition also revises several chapters to account for developments since the prior edition. In this book, the design principles behind the techniques are emphasized, including the bias-variance trade-off and its influence on the design of ensemble methods, enabling readers to solve applied problems more efficiently and effectively. This book also includes methods for optimization, risk estimation, model selection, and dealing with biased data samples and software limitations -- essential elements of most applied projects. Written by an expert in the field, this important resource:
- Illustrates many classification methods with a single, running example, highlighting similarities and differences between methods
- Presents side-by-side Python and 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 on both technical and ethical topics
- Details classification techniques including likelihood methods, prototype methods, neural networks, classification trees, and support vector machines
A volume in the popular Wiley Series in Probability and Statistics,
Machine Learning offers the practical information needed for an understanding of the methods and application of machine learning for advanced undergraduate and beginner graduate students, data science and machine learning practitioners, and other technical professionals in adjacent fields.
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 almost thirty years' experience in using Machine Learning, Statistics, and Mathematics to solve real-world problems. He is currently a Data Science Subject Matter Expert at the National Security Agency, where he has also served as Technical Director of Mathematics Research and in other senior technical and leadership roles.
