Curriculum

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Descriptions

MLDS 490: Bayesian Methods

Description

This course offers a rigorous yet practical exploration of Bayesian reasoning for data-driven inference and decision-making. Students will gain a deep understanding of probabilistic modeling, and Bayesian Networks - powerful tools for modeling uncertainty, learning from data, and making informed decisions under risk. Topics include causal inference, graph-based reasoning, learning Bayesian Networks from data, and advanced methods of Bayesian learning using Markov Chain Monte Carlo. The course emphasizes diverse real-world applications across engineering, healthcare, business, and the social sciences. By the end, students will be equipped to train probabilistic models, uncover causal relationships, and support robust decision-making using Bayesian methods.