The Statistical Machine Learning Textbook is a resource intended for readers interested in a concise mathematically rigorous introdution to statistical machine learning literature. A major objective of this advanced undergraduate textbook is to introduce a modular theory-based design strategy for supporting the mathematical analysis and design of machine learning algorithms.

 

The goal of this website is to provide you with supplementary materials to help you master the chapters from Statistical Machine Learning Textbook and help you with using the SML MATLAB software that came with the textbook.  

Contents

Part I: Inference and Learning Machines

  1. Examples of Machine Learning Algorithms
  2. Concept Models
  3. Formal Machine Learning Algorithms

Part II: Deterministic Machine Behavior

  1. Linear Machines
  2. Vector Calculus
  3. Time Invariant Optimization Algorithms
  4. Time Varying Optimization Algorithms

Part III: Stochastic Machine Behavior

  1. Random Vectors and Random Functions
  2. Stochastic Sequences
  3. Probability Models of Data Generation
  4. Monte Carlo Markov Chain Algorithms
  5. Adaptive Stochastic Approximation Learning Algorithms

Part IV: Generalization Performance

  1. Statistical Learning Objective Functions
  2. Simulation Methods: Generalization Performance
  3. Analytic Formulas: Generalization Performance
  4. Model Selection and Evaluation

SML Software Guide

You are also encouraged to ask questions and join the discussions regarding the textbook at Statistical Machine Learning LinkedIn Forum.

If you notice any typos or have suggestions that needs to be included in the textbook, please feel free to contact the author directly by e-mail at: golden@utdallas.edu