{"product_id":"statistical-machine-learning-richard-golden-9781138484696","title":"Statistical Machine Learning: A Unified Framework","description":"\u003cp\u003eThe 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. \u003cb\u003eStatistical Machine Learning: A Unified Framework\u003c\/b\u003e 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.\u003c\/p\u003e\u003cp\u003eFeatures: \u003c\/p\u003e\u003cul\u003e \u003cp\u003e \u003c\/p\u003e \u003cli\u003eUnified empirical risk minimization framework supports rigorous mathematical analyses of widely used supervised, unsupervised, and reinforcement machine learning algorithms\u003c\/li\u003e \u003cp\u003e \u003c\/p\u003e \u003cli\u003eMatrix calculus methods for supporting machine learning analysis and design applications\u003c\/li\u003e \u003cp\u003e \u003c\/p\u003e \u003cli\u003eExplicit conditions for ensuring convergence of adaptive, batch, minibatch, MCEM, and MCMC learning algorithms that minimize both unimodal and multimodal objective functions\u003c\/li\u003e \u003cp\u003e \u003c\/p\u003e \u003cli\u003eExplicit conditions for characterizing asymptotic properties of M-estimators and model selection criteria such as AIC and BIC in the presence of possible model misspecification\u003c\/li\u003e \u003c\/ul\u003e\u003cp\u003eThis 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.\u003c\/p\u003e\u003cp\u003eAbout the Author: \u003c\/p\u003e\u003cp\u003eRichard 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.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAuthor:\u003c\/b\u003e Richard Golden\u003cbr\u003e\u003cb\u003eISBN-10:\u003c\/b\u003e 1138484695\u003cbr\u003e\u003cb\u003eISBN-13:\u003c\/b\u003e 9781138484696\u003cbr\u003e\u003cb\u003ePublisher:\u003c\/b\u003e CRC Press\u003cbr\u003e\u003cb\u003eLanguage:\u003c\/b\u003e English\u003cbr\u003e\u003cb\u003ePublished:\u003c\/b\u003e 07\/02\/2020\u003cbr\u003e\u003cb\u003ePages:\u003c\/b\u003e 506\u003cbr\u003e\u003cb\u003eFormat:\u003c\/b\u003e Hardcover\u003cbr\u003e\u003cb\u003eWeight:\u003c\/b\u003e 2.77lbs\u003cbr\u003e\u003cb\u003eSize:\u003c\/b\u003e 10.10h x 6.90w x 1.00d\u003cbr\u003e\u003cbr\u003e\u003cb\u003eReview Citation(s): \u003c\/b\u003e\u003cbr\u003e\u003ci\u003eChoice\u003c\/i\u003e 05\/01\/2021","brand":"Richard Golden","offers":[{"title":"Hardcover","offer_id":48113277436159,"sku":"9781138484696","price":165.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0662\/2982\/9887\/files\/img_d46b6a85-00f4-4757-86e5-85dfe693b590.jpg?v=1769614040","url":"https:\/\/www.whiterainbookhouse.com\/products\/statistical-machine-learning-richard-golden-9781138484696","provider":"WR Book House","version":"1.0","type":"link"}