(Spring 2026) CSC 196 : Analyzing Data

This course provides a rigorous yet accessible introduction to the fundamental concepts of probability and statistics, equipping students with the tools to analyze and interpret data and to reason about uncertainty. Topics include probability rules and counting methods, discrete and continuous random variables, expectation and variance, sampling distributions, and the Central Limit Theorem. Statistical topics cover descriptive statistics, estimation, confidence intervals, correlation, and hypothesis testing. Throughout the course, students will apply theory to real-world problems using analytical methods and computational tools. By the end of the term, students will be able to design basic experiments, interpret data responsibly, and make informed decisions based on probabilistic reasoning.

(Fall 2025) 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.

 

(Spring 2025) 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.

Previous Offering: (Fall 2023)

(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)

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.