CS4315 - Intelligent Systems Design

CS 4315 Intelligent Systems Design (3 semester credit hours) Mathematical analysis of estimation (learning) and inference in nonlinear high-dimensional deterministic and stochastic dynamical systems widely used in the fields of machine learning and artificial neural network modeling. The course uses a mathematical statistics perspective that emphasizes parametric statistical methods for the direct analysis of generalization performance in artificially intelligent dynamical systems. Topics include: (1) Markov Random Field probability representations, and (2) asymptotic mathematical statistical theory for: parameter estimation, model selection, and hypothesis testing. Prerequisites: (STAT 3341 or equivalent) and MATH 2418 and MATH 2419. (Same as CGS 4315) (3-0) T