(Fall 2024) 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 2023) (Spring 2022) (Fall 2020)

(Spring 2024) CSC 696H-1 : Advanced Topcis in Artificial Intelligence

Seminar course surveying recent literature for probabilistic methods in machine learning. The course focuses on models and methods for inference and decision making. Topics covered include variational methods, advanced MCMC, Bayesian Deep Learning, uncertainty quantification, and representation learning.

Previous Offerings: (Fall 2022) (Fall 2019)

 

(Fall 2023) CSC 480 / 580 : Principles of Machine Learning

This course covers the fundamentals of machine learning. Machine learning is all about automatic ways for computers to find patterns in datasets; students will learn both advantages and unique risks that this approach offers. Students will learn the computational methods, algorithms, and perspectives which underlie current machine learning methods, and how to derive and implement many of them.

Source: Robinson, E. and Nolis, J.

(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.