CS 6786

Probabilistic Machine Learning for Scientific Discovery

Course Description

Course information provided by the 2026-2027 Catalog.

As machine learning is increasingly applied in science and engineering, there is a growing need for methods that can model complex data assumptions, quantify uncertainty, and make reliable predictions and decisions. This course covers foundational and recent developments in probabilistic machine learning, including generative modeling, uncertainty quantification, sampling, and optimization. We will also survey research papers and their applications to scientific domains, such as chemistry and physics. The course culminates in a research project inspired by a scientific application of probabilistic machine learning.

Prerequisites CS 1110 or CS 1112 and CS 3780 or CS 5780 or equivalent and STSCI 3080 or equivalent.

Last 4 Terms Offered (None)

Learning Outcomes

• Students will develop an understanding of foundational methods in generative modeling, uncertainty quantification, optimization, sampling.
• Students will examine recent research papers in probabilistic machine learning and their application to scientific domains, including chemistry and physics.
• Students will demonstrate their mastery of the material through a course project motivated by an important scientific application.

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