Theories and methods for learning from data, covering supervised and unsupervised learning, regression, k-means, KNN, neural nets and deep learning, transformers, recurrent neural nets, adversarial learning, Bayesian learning, and genetic algorithms. Underpinnings include perceptrons, backpropagation, attention, and transformers; each student creates a term project.

Level

grad

Department

MET

Credits

4

Prerequisites

MET CS 521 & one of 577/622/673/682