Statistical Machine Learning

Statistical Machine Learning Book Contents

BOOK OVERVIEW

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
Copyright 2019-2021  by Richard M. Golden. All rights reserved.
Statistical machine learning framework involves using data to select a probability distribution from the learning machine's probability model.

The statistical machine learning framework. The Data Generating Process (DGP) generates observable training data from the unobservable environmental probability distribution Pe. The learning machine observes the training data and uses its beliefs about the structure of the environmental probability distribution in order to construct a best-approximating distribution P of the environmental distribution Pe. The resulting best-approximating distribution of the environmental probability distribution supports decisions and behaviors by the learning machine for the purpose of improving its success when interacting with an environment characterized by uncertainty.

Figure 1.1 from Statistical Machine Learning: A unified framework by Richard M. Golden.  Copyright 2021 by Richard M. Golden. Written permission from Richard M. Golden is required to download or use Figure 1.1.
 

Chapter by Chapter Overview Provided in Podcast LM101-078 (www.learningmachines101.com)