CGS4315 - Intelligent Systems Design

CGS 4315 Intelligent Systems Design (3 semester credit hours) This advanced machine learning course covers mathematics essential for the analysis and design of unsupervised, supervised, and reinforcement machine learning algorithms including deep learning neural network models formulated within a statistical empirical risk minimization framework. Topics include: convergence analysis of adaptive and batch learning algorithms, Bayes Nets and Marko fields, Monte Carlo Markov Chain inference algorithms, bootstrap sampling methods, and the statistical analysis of generalization performance. Unsupervised, supervised, and reinforcement machine learning applications are emphasized throughout the course. Prerequisite: CGS 4314 or CS 4314. (Same as CS 4315) (3-0) T