In particular, detailed coverage is given to the design considerations involved in automatic theorem proving, natural language understanding, and machine learning. Cross-listed as CS 716. Applications are drawn from water resources, environmental, industrial and other engineering areas. Topics discussed and papers reviewed are selected at the first meeting. Students are expected to read every paper before it is presented.
Class size is limited.
An in-depth study of time series analysis and forecasting models from a statistical and engineering perspective. Emphasizes the process of stochastic model building including model identification, estimation, and model diagnostic checking. Topics are determined by student and faculty interests and may include model and simulation theory, validation, experiment design, output analysis, variance-reduction techniques, simulation optimization, perturbation analysis, parallel and distributed simulation, intelligent simulation systems, animation and output visualization, and application domains. SYS 605 or instructor permission Characterization and analysis of problem solving strategies guided by heuristic information. The course links material from optimization, intelligence systems, and complexity analysis.