Probability and Statistics Methods

Catalog description

This course presents principles and methods in Artificial Intelligence rooted in Applied Statistics, encompassing probability theory and big data analysis. It introduces Classical and Bayesian methodologies for parameter estimation, uncertainty quantification, and model testing within an Artificial Intelligence framework. Explored techniques comprise Bayesian networks, regression, self-organizing maps, decision trees, and ensemble methods. Additionally, the course examines both the possibilities and challenges of Artificial Intelligence applications relying on big data analysis. Prerequisite Course(s): Prerequisites: CSCI 353. Corequisites: CSCI 425. Classroom Hours - Laboratory and/or Studio Hours – Course Credits: 3-0-3