Statistical Machine Learning: A Unified Framework

ISBN-13: 9781138484696 / Angielski / Twarda / 2020 / 506 str.

Richard Golden

Opis

This book is a text intended for advanced undergraduates or graduate students which provides theoretical tools for analyzing and designing a large class of supervised, unsupervised, and reinforcement statistical machine learning algorithms using classical theorems from the fields of nonlinear optimization theory and mathematical statistics.

Spis treści

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

O autorze

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.