SYST/IT Doctoral Courses

796, 797 Directed Reading and Research (1-3:0:0).

Reading and research on a specific topic in information technology under the direction of a faculty member. May be repeated as needed.

819 Computational Models for Probabilistic Inference (3:3:0).
Prerequisite: SYST 664.

Graphical models for encoding conditional independence assumptions in a multivariate discrete probability distribution. Includes computational methods for updating probabilities when evidence is observed on some variables in the model. Algorithms for finding the most probable instantiation of the network. Applications in expert systems and decision analysis.

842 Models of Probabilistic Reasoning (3:3:0).
Prerequisite: STAT 544 and OR 681.

Survey of alternative views about how incomplete, inconclusive, and possibly unreliable evidence might be evaluated and combined. Among the views discussed are the Bayesian, Baconian, Shafer-Dempster, and Fuzzy systems for probabilistic reasoning.

850 Seminar: Topics in Systems Integration Engineering (3:3:0).
Prerequisite: SYST 720 or equivalent.

Analysis of the Systems Integration life cycle and the tools, techniques, and methods that contribute to the design, development, application, and evaluation of approaches to systems integration. Reviews the current technological advances that support systems integration methods, including functional and nonfunctional SI requirements, risk assessment and risk management, internal protest avoidance mechanisms, and protest management. May be repeated when the topic is different.

888/ECE 753 Distributed Estimation and Multisensor Tracking and Fusion (3:3:0).
Prerequisite: ECE 734 or SYST 611.

Centralized and distributed estimation theory, hierarchical estimation, tracking and data association, multisensor multitarget tracking and fusion, distributed tracking in distributed sensor networks, track-to-track association and fusion, and Bayesian networks for fusion.