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Course Criteria
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3.00 Credits
This course is designed to stimulate critical thinking about environmental science principles using computational modeling methodologies. Some topics to be covered included groundwater and contaminant transport, phosphorus cycling in surface waters, and global climate change. Prerequisite: CSAC/CS 245 or permission of instructor. (Same course offered as ENVS 393.) Fall, alternating years.
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3.00 Credits
This course provides a modeling approach in the fields of psychology and neuroscience. Topics may include decision making, learning models, neuro imaging techniques, and neural networks. (Same course offered as PSYCH 394.)
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3.00 Credits
This course is designed to introduce some of the computational methods used in physics. Students will work in groups and are expected to use prior knowledge from calculus, general physics, and computational science to develop appropriate strategies for solving problems. Use of a combination of different methodologies (algebraic, numerical, graphical/visual) is expected. (Same course offered as PHYS 396.)
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3.00 Credits
This course provides a general introduction to the terminology, methodology, and applications of scientific visualization. Methods for visualizing surface and volumetric data from a variety of scientific fields including both static medical data and time varying data are presented. The standard generic pipeline for converting numerical data to visual representations is presented using the VTK software package. Prerequisite: CS 161. (Same course offered as CS 397.)
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3.00 Credits
Students explore the solution methodology of problems in computational science with an emphasis on numerical techniques. Topics include error analysis, numerical integration and differentiation, FFTs, solutions of linear systems, and numerical solutions of ODEs. Prerequisites: CSAC/CS 245, MATH 231. Recommended: CSAC 335/MATH 335. (Same course offered as MATH 435.) Offered spring semester in odd years.
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3.00 Credits
A capstone research experience usually comprising a comprehensive literature review, design, and implementation of computational science techniques to solve a problem in the behavioral, computer, financial, mathematical, natural, physical, or social sciences. Prerequisites: CSAC 435 and at least one CSAC Elective course. This course is repeatable for additional credit.
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3.00 Credits
Study of artificial intelligence with emphasis on production systems, search strategies, heuristics, predicate calculus and rule-based systems. One area of artificial intelligence is investigated in detail along with the introduction of an appropriate programming language. Prerequisite: CS 161. Offered when there is sufficient demand.
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3.00 Credits
Formal language theory including languages, grammars, regular expressions, finite automata, pushdown automata and Turing theory. Prerequisites: CS 161 and 200. Offered when there is sufficient demand.
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3.00 Credits
(3). Introduction to the use of high-performance computing systems in science and engineering. The physical aspects of a variety of problems are surveyed and techniques for solving the problems on a variety of high-performance computers are analyzed. Prerequisite: C or better in CS 376. Offered spring semester in odd years. (Same course offered as CSAC 476.)
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3.00 Credits
Local and wide area networking including: protocols, standards, media, topologies, layered networking models, hardware and software. Prerequisite: CS 161. Offered fall semester in odd years.
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