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Statistical Machine Learning: A Unified Framework (Chapman & Hall/CRC Texts in

Description: 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.

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Location: Matraville, NSW

End Time: 2025-01-05T01:45:53.000Z

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Statistical Machine Learning: A Unified Framework (Chapman & Hall/CRC Texts inStatistical Machine Learning: A Unified Framework (Chapman & Hall/CRC Texts in

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Return shipping will be paid by: Buyer

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EAN: 9781138484696

UPC: 9781138484696

ISBN: 9781138484696

MPN: N/A

Item Height: 2.5 cm

Number of Pages: 506 Pages

Language: English

Publication Name: Statistical Machine Learning : a Unified Framework

Publisher: CRC Press LLC

Publication Year: 2020

Subject: Machine Theory, Probability & Statistics / General, General

Type: Textbook

Item Weight: 40 Oz

Author: Richard Golden

Item Length: 10 in

Subject Area: Mathematics, Computers, Référence, Science

Series: Chapman and Hall/Crc Texts in Statistical Science Ser.

Item Width: 7 in

Format: Hardcover

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