Artificial Intelligence Fundamentals

Catalog description

Introduces core artificial intelligence techniques, problem-solving methods, search algorithms, symbolic AI, and knowledge representation. Explores search strategies, uncertainty quantification, and gradient descent. Covers supervised and unsupervised learning techniques, including performance metrics and evaluation for both classification and regression problems. Prerequisite(s): ( AI 270 or CS 311 ) and either MATH 315 or one of the following combinations ( MATH 142 or MATH 242 or MATH 342 or MATH 441 or MATH 442 or BIOL 215 ; and either MATH 264 or MATH 374 ).