Statistical Machine Learning by Richard Golden

Statistical Machine Learning by Richard Golden

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.

'In summary, readers of this book need to have fair knowledge of statistics, computer science, electrical engineering, or applied mathematicsHowever, 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

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.

SKU	Nicht verfügbar
ISBN 13	9781138484696
ISBN 10	1138484695
Titel	Statistical Machine Learning
Autor	Richard Golden
Serie	Chapman And Hall Crc Texts In Statistical Science
Buchzustand	Nicht verfügbar
Verlag	Taylor & Francis Ltd
Erscheinungsjahr	2020-07-02
Seitenanzahl	506
Hinweis auf dem Einband	Die Abbildung des Buches dient nur Illustrationszwecken, die tatsächliche Bindung, das Cover und die Auflage können sich davon unterscheiden.
Hinweis	Nicht verfügbar