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Statistical Machine Learning: A Unified Framework (Chapman & Hall/CRC Texts in Statistical Science) - Hardcover
- PublisherChapman and Hall/CRC
- Publication date2020
- ISBN 10 1138484695
- ISBN 13 9781138484696
- BindingHardcover
- LanguageEnglish
- Edition number1
- Number of pages506
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STATISTICAL MACHINE LEARNING : A UNIFIED FRAMEWORK
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Statistical Machine Learning: A Unified Framework (Chapman & Hall/CRC Texts in Statistical Science)
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Statistical Machine Learning : A Unified Framework
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Statistical Machine Learning : A Unified Framework
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Statistical Machine Learning : A Unified Framework
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Statistical Machine Learning (Hardcover)
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Taylor & Francis Ltd, London, 2020
ISBN 10: 1138484695
ISBN 13: 9781138484696
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Hardcover. Condition: new. Hardcover. 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 misspecificationThis 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. This book is a text intended for advanced undergraduates or graduate students which provides theoretical tools for analyzing and designing a large class of supervised, unsupervised, and reinforcement statistical machine learning algorithms using classical theorems from the fields of nonlinear optimization theory and mathematical statistics. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9781138484696
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