Introduction to Machine Learning

CS 464 is your first principled look at how machines learn patterns from data — the course treats classification, regression, and clustering as one unified problem of fitting models under uncertainty, so you spend as much time on the probability and loss-function reasoning behind an algorithm as on the algorithm itself. Expect three substantive homeworks (math derivations plus Python/PyTorch implementations on Colab), a midterm, a final, and a term project where you take a real dataset end-to-end. It assumes you are comfortable with linear algebra, probability, and Python, and it is the standard gateway into CS 4xx/5xx electives on deep learning, NLP, and computer vision — basically the prerequisite that makes the rest of the AI track legible.