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            Probability & Statistics

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            Statistical Machine Learning: A Unified Framework

            Statistical Machine Learning: A Unified Framework (Other book format / Other)

            By Golden, Richard

            • RRP: $98.99
            • $98.99
            • Delayed Release

            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 provides students, engineer...s, 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. Book 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
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            ISBN 9780367494223
            Released NZ 1 May 2020
            Publisher Taylor & Francis Ltd
            Interest Age General Audience
            Series Chapman & Hall/CRC Texts in Statistical Science
            Availability
            Pre-order title, release date has been delayed
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            Categorical Data Analysis by Example: Hands on approach using R
             

            Categorical Data Analysis by Example: Hands on approach using R (Trade Paperback / Paperback)

            By Anderson, Faye

            • RRP: $82.50
            • $64.29
            • Save $18.21
            • In Stock US

            This is an applied handbook on analyzing and modeling categorical data using R. Every topic is explained using examples. Although the examples are presented in R, their results and interpretations are applicable to any other statistical software. The examples cover exploratory da...ta analysis, contingency tables, hypothesis testing, ordinal data, logistic regression, multinomial logistic regression, ordinal logistic regression, and generalized models.
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            ISBN 9798650325352
            Released NZ 1 Jun 2020
            Publisher Independently Published
            Interest Age General Audience
            Series Statistics by Example
            Availability
            Internationally sourced; ships 6-12 working days
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