HW0 checks CS181 prerequisites in four problems — y=Xw solvability, optimizing an objective, reasoning about randomness, and OLS in Python. The problem that slows you down most is the gap to patch before HW1.
HW1 uses an 800k‑year ice‑core temperature dataset to implement three regression models (OLS, RBF kernel, MLP) and compare them on the same data, laying the groundwork for later classification and deep‑learning assignments.
Lecture 7 applies ERM to linear functions and squared loss, moves from a one-dimensional slope to `argmin ||y-Xθ||²`, and derives the normal equation when `XᵀX` is invertible.
Linear regression is more than a best-fit line: Chapter 1 connects squared loss to gradient descent, normal equations, maximum likelihood, and locally weighted regression.
HW3 is written work: a decision-tree review followed by K-NN, Perceptron, and Linear Regression through inductive bias, errors, and model selection.