Current Courses


(Fall 2020) CSC 535 : Probabilistic Graphical Models
Introductory course to probabilistic graphical models in machine learning. This course will cover probabilistic modeling and algorithmic approaches to probabilistic inference and parameter learning. Topics will include Bayesian modeling and probability, message passing inference algorithms, belief propagation, variational inference, Markov chain Monte Carlo sampling, expectation maximization, dynamical systems, and Bayesian nonparametrics.

(Fall 2019) CSC 665-1 : Advanced Topcis in Probabilistic Graphical Models
Seminar course surveying recent literature for automated reasoning in probabilistic graphical models. The course focuses on algorithms for inference and decision making. Topics covered include variational methods, advanced MCMC, Bayesian nonparameterics, Bayesian optimization.


© Jason Pacheco, 2019