Statistical Learning and Data Analytics

EEE 485 is where probability and linear algebra stop being abstract and start becoming the machinery you use to make predictions from messy real data — the course treats learning as parameter estimation under uncertainty, comparing the Bayesian and frequentist lenses on the same problems. You'll work through regression, GLMs, neural nets, clustering, and graphical models on the theory side, while implementing algorithms end-to-end in a term project alongside four quizzes and the usual midterm/final. It's the natural bridge from EEE's probability and signals background into modern ML, and the foundation most people lean on before touching deep learning or research-flavored electives.