Unit 1
What Is Statistical Learning?
01Preview
Function Approximation
Establishes Y = f(X) + ε and the split between reducible and irreducible error, using two concrete running examples.
Open module →02Locked
Prediction vs. Inference
The "why estimate f?" fork — a Prediction/Inference toggle on one fitted model that recurs through later modules.
03Locked
Supervised vs. Unsupervised Learning
A label-visibility toggle that turns the same scatter into a classification task or a clustering problem.
04Locked
Regression vs. Classification
Response type × method flexibility, plus the flexibility/interpretability spectrum — parametric and non-parametric side by side.
05Locked
Assessing Model Accuracy
A flexibility slider driving the classic train/test MSE U-curve, across three preset true functions.
06Locked
The Bias-Variance Trade-off
Resampled fits and a live bias²/variance/MSE decomposition — the payoff explaining Module 5's U-shape.
Unit 2 — In development
Fitting the Model — loss functions, gradient descent, closed-form OLS, the bootstrap.