Results - CS 6347 - UTD TRENDS

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Teaching in Spring 2026

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Statistical Methods in AI and Machine Learning

CS 6347

Erik Jonsson School of Engineering and Computer Science

Introduction to the probabilistic and statistical techniques used in modern computer systems. Probabilistic graphical models such as Bayesian and Markov networks. Probabilistic inference techniques including variable elimination, belief propagation and its generalizations, and sampling-based algorithms such as importance sampling and Markov Chain Monte Carlo sampling. Statistical learning techniques for learning the structure and parameters of graphical models. Sequential models such as Hidden Markov models and Dynamic Bayesian networks. 3 credit hours.

Prerequisites: (CS 3341 or equivalent) and (CS 5343 or equivalent).

Offering Frequency: Each year

Grades: 302

Median GPA: A

Mean GPA: 3.623

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