Training statistical learning models

Bias-variance, validation, resampling

Under- and over-fitting; the training/validation/test split; k-fold cross-validation and the bootstrap. Foundations for every method we’ll see later in the course.

📖 Assigned reading: ISLR §2.2, ch. 5

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📊 Slides

• 🖥️ Clean slides

🧪 Lab

Live, in-browser R lab — runs entirely in your browser via webR, no local R install needed. Edit any code cell and re-run it; a “Download my code” button captures whatever you’ve written before you leave.

• 🧪 Lab 2 (interactive)

⭐ Extras

Optional supplementary material:

• 📄 Bootstrap
• 📄 Permutation Test