Probabilistic Machine Learning

STA 561D

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. Prerequisite: Linear algebra, Statistical Science 250 or Statistical Science 611. Instructor: Staff
Curriculum Codes
  • QS
Cross-Listed As
  • COMPSCI 571D
  • ECE 682D
Typically Offered
Spring Only