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Course Criteria
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1.00 - 3.00 Credits
Seminar topics in computer science, computer information systems, or operations research. Does not apply toward fulfillment of the requirements of the Master of Systems Analysis. Credit/no-credit only. Prerequisite: permission of instructor.
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3.00 Credits
Use of deterministic and stochastic mathematical models to study and optimize systems. This course includes an introduction to mathematical modeling and the study of linear programming, network models, Markov processes and queuing theory. Students will use computer software for model construction and problem solving. Prerequisites: credit in calculus, probability, statistics, or permission of instructor.
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3.00 Credits
Study of advanced networking techniques, client/ server programming, and distributed processing. Critical analysis of these areas develops as students learn the strengths and weaknesses of these technologies through assigned programming projects. Prerequisite: CSA 608 or equivalent.
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3.00 Credits
Study of hardware, software, and algorithms used in computer graphics. Instruction emphasizes the use of a scene graph-based API. Topics will include lighting, blending, texture mapping, non real-time rendering techniques such as radiosity and ray tracing. Prerequisite: CSA 606 or equivalent.
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3.00 Credits
Foundational theories for software engineering; Topics include project management, modeling notations, refinements processes, verification and validation, and evolution. Prerequisite(s): CSA 348 or equivalent.
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3.00 Credits
Expert Systems architecture, knowledge representation, and inference techniques. Provides a combination of both theory and practice in expert systems. An Expert Systems tool is used to implement an expert system. Prerequisite: CSA 274 or 606.
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3.00 Credits
Introduction to information storage and retrieval (IR). Indexing, clustering, signature generation. Retrieval approaches: inverted files, cluster-based retrieval, signature files, hypertext, and multimedia systems. Special hardware for IR. Web-based IR and information filtering. Prerequisite: CSA 606 or equivalent.
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3.00 Credits
Concepts and algorithms of machine learning including version-spaces, decision trees, instance-based learning, networks, evolutionary computation, Bayesian learning and reinforcement learning. Prerequisite: CSA 606 or equivalent.
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3.00 Credits
Advanced simulation topics such as heuristic optimization techniques for simulation and distributed simulation as well as classical techniques such as experimental design, variance reduction, and comparison of alternative designs. Students will embed some of these techniques in an object-oriented simulation program. The effectiveness of these techniques will be investigated in complex simulation models such as queuing networks. Prerequisite: CSA 471/571.
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3.00 Credits
Research problems in computer science, computer information systems, or operations research, decided upon in consultation with the instructor and student's graduate adviser. Requires a public presentation of completed work. For grade only. Prerequisite: permission of instructor, student's graduate adviser, and graduate director.
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