ECE 6714 - Probabilistic Machine Learning

Effective Date 01/01/2023
Covers foundations of estimation theory and machine learning in a probabilistic modeling framework. Topics include frequentist and Bayesian estimation, analysis of estimators, linear regression, linear classification, graphical models, Markov models, sampling methods, and variational inference. Requires APMA 3100 or an equivalent course on Probability, familiarity with linear algebra, and Python programming.

Credits: 3
Grading Basis Graded