CS5780 Intro to Machine Learning 

The course provides an introduction to machine learning, focusing on supervised learning and its theoretical foundations. Topics include regularized linear models, boosting, kernels, deep networks, generative models, online learning, and ethical questions arising in ML applications.

Prerequisites/Corequisites Prerequisite: CS 2800, probability theory (e.g. BTRY 3080, ECON 3130, MATH 4710, ENGRD 2700) and linear algebra (e.g. MATH 2940), calculus (e.g. MATH 1920) and programming proficiency (e.g. CS 2110).

Fees Course fee: $30.

When Offered Fall, Spring.

CS6785 Deep Probabilistic and Generative Models

Generative models are a class of machine learning algorithms that define probability distributions over complex, high-dimensional objects such as images, sequences, and graphs. Recent advances in deep neural networks and optimization algorithms have significantly enhanced the capabilities of these models and renewed research interest in them. This course explores the foundational probabilistic principles of deep generative models, their learning algorithms, and popular model families, which include variational autoencoders, generative adversarial networks, autoregressive models, and normalizing flows. The course also covers applications in domains such as computer vision, natural language processing, and biomedicine, and draws connections to the field of reinforcement learning.

CS6784 Advanced Topics in Machine Learning (Deep Dive into Large Language Models)

Extends and complements CS 3780 (formally CS 4780) and CS 5780, giving in-depth coverage of new and advanced methods in machine learning.

Prerequisites CS 3780 or CS 5780 or equivalents or permission of instructor.