ECSE 4810 - Introduction to Probabilistic Graphical Models

This course covers topics related to learning and inference with different types of Probabilistic Graphical Models (PGMs). It also demonstrates the application of PGMs to different fields. The course covers both directed and undirected graphical models, both parameter and structure learning, and both exact and approximated inference methods.

Prerequisite: ECSE 2500 or ENGR 2600; proficiency in computer programming. Prior knowledge in pattern recognition or machine learning is a plus but is not required.

When Offered: FALL TERM, EVEN YEARS

Co-Listed: ECSE 6810. Students cannot receive credit for both this course and ECSE 6810.

Graded: GRADED

Credit Hours: 3