Current Courses

(Spring 2023) 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.

Previous Offering: (Spring 2022) (Fall 2020)

(Fall 2022) CSC 696H-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.

Previous Offering: (Fall 2019)

(Fall 2021) CSC 380 : Principles of Data Science
This course introduces students to the principles and tools of data science and provides a foundation for properly collecting and analyzing data to draw insights and to answer data-driven questions. The course has three main components: applied probability and statistics, data analysis and visualization, and machine learning.

© Jason Pacheco, 2019