Chapman and Hall/CRC
May 22, 2020 Forthcoming
Textbook - 536 Pages
ISBN 9781138484696 - CAT# K349248
Series: Chapman & Hall/CRC Texts in Statistical Science
For Instructors Request Inspection Copy
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.
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.
Part I: Inference and Learning Machines. Chapter 1: Examples of Machine Learning Algorithms. Chapter 2: Concept Models. Chapter 3: Formal Machine Learning Algorithms. Part II: Deterministic Machine Behavior. Chapter 4: Linear Machine Behavior. Chapter 5: Vector Calculus. Chapter 6: Time-Invariant Optimization Algorithms. Chapter 7: Time-Varying Optimization Algorithms. Part III: Stochastic Machine Behavior. Chapter 8: Random Vectors and Random Functions. Chapter 9: Stochastic Convergence. Chapter 10: Probabilistic Models of Data Generating Processes. Chapter 11: Monte Carlo Markov Chain Machines. Adaptive Stochastic Approximation Algorithms. Part IV: Generalization Performance. Chapter 13: Statistical Learning Objective Functions. Chapter 14: Simulation Methods: Generalization Performance. Chapter 15: Analytic Formulas: Generalization Performance.
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