Statistical Machine Learning

A Unified Framework

Richard Golden

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Hardback

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English

CRC Press
02 July 2020

Probability & statistics; Machine learning

Series: Chapman & Hall/CRC Texts in Statistical Science

The recent rapid growth in the variety and complexity of new machine learning architectures requires the development of improved methods for designing, analyzing, evaluating, and communicating machine learning technologies. Statistical Machine Learning: A Unified Framework provides students, engineers, and scientists with tools from mathematical statistics and nonlinear optimization theory to become experts in the field of machine learning. In particular, the material in this text directly supports the mathematical analysis and design of old, new, and not-yet-invented nonlinear high-dimensional machine learning algorithms.

Features:

Unified empirical risk minimization framework supports rigorous mathematical analyses of widely used supervised, unsupervised, and reinforcement machine learning algorithms

Matrix calculus methods for supporting machine learning analysis and design applications

Explicit conditions for ensuring convergence of adaptive, batch, minibatch, MCEM, and MCMC learning algorithms that minimize both unimodal and multimodal objective functions

Explicit conditions for characterizing asymptotic properties of M-estimators and model selection criteria such as AIC and BIC in the presence of possible model misspecification

This advanced text is suitable for graduate students or highly motivated undergraduate students in statistics, computer science, electrical engineering, and applied mathematics. The text is self-contained and only assumes knowledge of lower-division linear algebra and upper-division probability theory. Students, professional engineers, and multidisciplinary scientists possessing these minimal prerequisites will find this text challenging yet accessible.

About the Author:

Richard M. Golden (Ph.D., M.S.E.E., B.S.E.E.) is Professor of Cognitive Science and Participating Faculty Member in Electrical Engineering at the University of Texas at Dallas. Dr. Golden has published articles and given talks at scientific conferences on a wide range of topics in the fields of both statistics and machine learning over the past three decades. His long-term research interests include identifying conditions for the convergence of deterministic and stochastic machine learning algorithms and investigating estimation and inference in the presence of possibly misspecified probability models.

By: Richard Golden
Imprint: CRC Press
Country of Publication: United Kingdom
Dimensions: Height: 254mm, Width: 178mm,
Weight: 1.280kg
ISBN: 9781138484696
ISBN 10: 1138484695
Series: Chapman & Hall/CRC Texts in Statistical Science
Pages: 506
Publication Date: 02 July 2020
Audience: College/higher education , General/trade , Primary , ELT Advanced
Format: Hardback
Publisher's Status: Active

Richard M. Golden (Ph.D., M.S.E.E., B.S.E.E.) is Professor of Cognitive Science and Participating Faculty Member in Electrical Engineering at the University of Texas at Dallas. Dr. Golden has published articles and given talks at scientific conferences on a wide range of topics in the fields of both statistics and machine learning over the past three decades. His long-term research interests include identifying conditions for the convergence of deterministic and stochastic machine learning algorithms and investigating estimation and inference in the presence of possibly misspecified probability models.

Reviews for Statistical Machine Learning: A Unified Framework

'In summary, readers of this book need to have fair knowledge of statistics, computer science, electrical engineering, or applied mathematics. However, practicing professional engineers and scientists may find the material in this book to be a useful reference for verifying sufficient conditions for ensuring convergence of many commonly used deterministic and stochastic machine learning optimization algorithms; and for ensuring correct usage of commonly used statistical tools for characterizing sampling error and generalization performance. Further, since this book includes a large number of examples, teachers of a course on machine learning may also find this book useful. In addition, applied researchers involved with machine learning may also find this book helpful.' - Sada Nand Dwivedi, International Society for Clinical Biostatistics, 71, 2021