HW3 has two halves. The four written problems run from Newton's method for logistic regression and a convergence analysis of coordinate descent, through backprop, VJPs, and implicit differentiation, to an information-bottleneck view of what deep networks compress. The notebook has you build a BearTensor computation graph in NumPy, topological-sort backprop, and SGD/Momentum/Adam, then train a red-wine quality regressor with it, plus an optional Muon optimizer. Problems and notebook are public; official solutions and hidden tests are not.
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.
Lecture 6 recursively grows a tree from decision stumps, measures label uncertainty with entropy, and selects splits by `I(Y;W)=H(Y)-H(Y|W)`; this is computationally practical greedy ERM, not a global optimal-tree guarantee.