CPSI 33003 - Probabilistic Reasoning in AI

Three credit hours.

Introduces probabilistic models and decision-theoretic methods that enable AI systems to reason and act under uncertainty. Students learn to build and query Bayesian and Markov networks, implement approximate and temporal inference, and analyze the complexity of exact algorithms. Emphasis is placed on designing and coding graphical models that integrate uncertainty, evidence, and preferences, and prepare students for the applicatin of probabilistic AI techniques in real-world domains.

Prerequisites: STAT 35203.