This lecture splits test error into three terms: noise σ², squared bias, and variance. It then uses the closed-form solution of ridge regression to show how regularization moves each eigenvalue from λ to λ+ρ, trading a little bias for a large cut in variance. The second half covers double descent, ImageNet-V2, and choosing hyperparameters with a dev set, k-fold cross-validation, and successive halving.
Lec 14 recasts ridge as MAP under least squares plus a Gaussian prior, breaks down bias-variance, and closes by using Chatbot Arena to show how to read a paper. Lec 16 builds entropy from compression, moves on to KL and cross-entropy, and lands back on the logistic regression loss. The 3/17 midterm and its official solutions are public in the past-exams folder, with 6 problems, 56 points, 110 minutes, and 6 problem-by-problem walkthrough videos, so you can sit it as a mock exam.
CS181 Spring 2026 HW2 (due Feb 27) has four problems worth 90 points: train 10 logistic models on planet observations to see bias and variance, derive the MLE of a generative classifier, implement five classifiers on 27 loan applicants, and watch ridge reshape the loss surface under SGD, momentum, and Adam. The core skill is separating what one model's probability says from how much 10 models disagree.