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OverviewThe 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. Full Product DetailsAuthor: Richard GoldenPublisher: Taylor & Francis Ltd Imprint: CRC Press Weight: 1.280kg ISBN: 9781138484696ISBN 10: 1138484695 Pages: 506 Publication Date: 02 July 2020 Audience: College/higher education , General/trade , Tertiary & Higher Education , General Format: Hardback Publisher's Status: Active Availability: In Print  This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us. Table of ContentsPart I: Inference and Learning Machines. 1. A Statistical Machine Learning Framework 2. Set Theory for Concept Modeling 3. Formal Machine Learning Algorithms Part II: Deterministic Learning Machines 4. Linear Algebra for Machine Learning 5. Matrix Calculus for Machine Learning 6. Convergence of Time-Invariant Dynamical Systems 7. Batch Learning Algorithm Convergence Part III: Stochastic Learning Machines 8. Random Vectors and Random Functions 9. Stochastic Sequences 10. Probability Models of Data Generation 11. Monte Carlo Markov Chain Algorithm Convergence 12. Adaptive Learning Algorithm Convergence Part IV: Generalization Performance 13. Statistical Learning Objective Function Design 14. Simulation Methods for Evaluating Generalization 15. Analytic Formulas for Evaluating Generalization 16. Model Selection and EvaluationReviews'In summary, readers of this book need to have fair knowledge of statistics, computer science, electrical engineering, or applied mathematics. However, 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 'In summary, readers of this book need to have fair knowledge of statistics, computer science, electrical engineering, or applied mathematics. However, 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 Author InformationRichard 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. Tab Content 6Author Website:Countries AvailableAll regions |
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