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Full Product Details

Author:   Richard Golden
Publisher:   Taylor & Francis Ltd
Imprint:   CRC Press
Weight:   3.170kg
ISBN:  

9781138484696

ISBN 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  
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Table of Contents

Part 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 Evaluation

Reviews

'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 Information

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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