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Course Criteria
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3.00 Credits
Detailed study of a selected topic, determined by the current interest of faculty and students. Offered as required.
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3.00 Credits
Detailed study of a selected topic, determined by the current interest of faculty and students. Offered as required.
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1.00 - 12.00 Credits
Detailed study of graduate course material on an independent basis under the guidance of a faculty member.
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1.00 - 12.00 Credits
Formal record of student commitment to project research under the guidance of a faculty advisor. Registration may be repeated as necessary.
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3.00 Credits
Introduction to system and decision science with focus on theoretical foundations and mathematical modeling in four areas: systems (mathematical structures, coupling, decomposition, simulation, control), human inputs (principles from measurement theory and cognitive psychology, subjective probability theory, utility theory), decisions under uncertainty (Bayesian processing of information, Bayes decision procedures, value of information), and decisions with multiple objectives (wholistic ranking, dominance analysis, multiattribute utility theory).
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3.00 Credits
Under faculty guidance, students apply the principles of systems methodology, design, and management along with the techniques of systems and decision sciences to systems analysis and design cases. The primary goal is the integration of numerous concepts from systems engineering using real-world cases. Focuses on presenting, defending, and discussing systems engineering projects in a typical professional context. Cases, extracted from actual government, industry, and business problems, span a broad range of applicable technologies and involve the formulation of the issues, modeling of decision problems, analysis of the impact of proposed alternatives, and interpretation of these impacts in terms of the client value system.
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3.00 Credits
Provides a non-measure theoretic treatment of advanced topics in the theory of stochastic processes, focusing particularly on denumerable Markov processes in continuous time and renewal processes. The principal objective is to convey a deep understanding of the main results and their proofs, sufficient to allow students to make theoretical contributions to engineering research.
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3.00 Credits
In-depth study of major areas considered to be part of artificial intelligence. 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.
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3.00 Credits
The study of the philosophy, theory, methodology, and applications of systems engineering provides themes for this seminar in the art of reading, studying, reviewing, critiquing, and presenting scientific and engineering research results. Applications are drawn from water resources, environmental, industrial and other engineering areas. Throughout the semester, students make a presentation of a chosen paper, followed by a discussion, critique, evaluation, and conclusions regarding the topic and its exposition. Corequisite: SYS 601, 603, 605, or equivalent.
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3.00 Credits
This course provides an introduction to quantitative methods of measuring human performance in complex systems. The focus of the selected methodologies is based on providing insight into human performance in order to guide design and/or training. Assignments involve applying the methods to a human-machine system problem. If possible the application domain will involve the student’s research area of interest. Competency with regression techniques (e.g. SYS 421 or SYS 618) and statistics/design of experiments preferred.
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