Probabilistic Machine Learning

STA561D

Introduction to concepts in probabilistic machine learning with a focus on discriminative and hierarchical generative models. Topics include directed and undirected graphical models, kernel methods, exact and approximate parameter estimation methods, and structure learning. Prerequisites: Linear algebra, STA250 or STA611. One course / 3 units.

Curriculum Codes: 

QS