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Lectures and Topics

Lectures focus on the mathematical foundations of statistical learning and the behavior of the methods in practice. Both R and Python are supported in the lecture notes. Lecture materials are currently posted through Week 6; homework materials are available for Weeks 1 through 5.

Weekly Schedule

1. 01

   Course setup and AI tools

   Course introduction and structure GitHub Setup and AI Tools Discussion Questions: Procedures and Rules Homework 01
2. 02

   Training and Test Error

   Fixed-design prediction error and model selection Implementing linear model selection Homework 02
3. 03

   Ridge Regression and Optimization

   Ridge regression: stability through shrinkage Optimization, tuning, and cross-validation Homework 03
4. 04

   Lasso and Variable Selection

   Lasso, sparsity, and correlated predictors Homework 04
5. 05

   K-Nearest Neighbors

   K-Nearest Neighbors The Curse of Dimensionality Homework 05
6. 06

   Classification Error and Evaluation

   Classification: Probabilities, Decisions, and Learning Error Evaluating Classification Models

Computational notes

Both R and Python are supported throughout the lecture notes. Refer to Week 1 for instructions on AI agents, reusable skills, and the course GitHub repository.