L2 starts from one question: a computer sees a grid of numbers between 0 and 255, so how does it recognize a cat? Hand-written rules don't scale, so the course switches to a data-driven approach: collect data, train, evaluate on new images. The first classifier, kNN, teaches how to split train/val/test, but pixel distances carry no meaning. The second, the linear classifier f(x,W)=Wx+b, can be read three ways (algebraic, visual as templates, geometric as hyperplanes). Softmax turns its scores into probabilities, and the loss is the negative log probability of the correct class.
HW2 doesn't ask you to write a classifier. You write prompts and a pipeline so that an open LLM running on a Colab T4 (by default a 4-bit GGUF of gemma-3-12b-it) plans, codes, runs, and debugs a 10-class MyGO & Ave Mujica character face classifier on its own. The starter code is adapted from AIDE: an Interpreter runs code, a Node records each version, a Journal forms the solution tree, and the Agent decides whether to draft, debug, or improve next. The first thing worth noticing: the starter's evaluation is empty. Every version is marked metric 1.0 and not buggy, so the tree search picks blindly until you fill it in. The rules are strict: "the LLM agent is your representative", and you may not hand-edit code or prediction files.