Statistical Machine Learning

Preface

This repository presents a structured overview of statistical machine learning, with a focus on fundamental concepts and ideas in predictive modeling, model evaluation, and modern learning methods.

It emphasizes statistical principles for inference and uncertainty quantification, providing a conceptual foundation for understanding and developing reliable and interpretable machine learning systems.

0. Overview

• Overview

1. Linear Models

• Linear Regression

2. Model Evaluation

• Error, Bias–Variance Tradeoff, and Validation
• Model Assessment

3. Regularization

• Regularization Methods

4. Classification

• Classification Methods

5. Nonparametric Methods

• Nonparametric Methods I
• Nonparametric Methods II
• Nonparametric Methods III

6. Resampling Methods

• Bootstrap

7. Model Averaging and Ensemble Methods

• Model Averaging and Bagging

8. Tree-Based Methods

• Decision Trees
• MARS and HME

9. Ensemble Methods

• Random Forests
• Boosting Methods I
• Boosting Methods II

10. Neural Networks

• Neural Networks I
• Neural Networks II