Machine Learn (ECE)

Undergraduate credit

Fundamentals of supervised, unsupervised, and sequential learning, including linear and nonlinear regression, logistic regression, support vector machines and kernel methods, decision trees, ensemble methods, neural networks and deep learning, dimension reduction, clustering, and probabilistic models. Emphasis on algorithms and their derivation from fundamental principles. Includes applications to real-world data. Projects are overseen/graded by faculty and may also involve mentoring by representatives from external organizations.