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ARTSVIS 266S: Body as the Computer
3.00 Credits
Duke University
Weekly discussions/lectures related to different disciplinary understandings of the body, exploring new computational and aesthetic paradigms for brain/mind/body/ environment relations, and working towards articulating bridging languages enabling researchers to talk across disciplines. Students required to participate in ongoing discussion, develop particular aspects of research and write a major research paper. Instructor: Seaman
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ARTSVIS 269S: Special Topics in Visual Arts
3.00 Credits
Duke University
Special Topics in Visual Arts. Subject varies from year to year. One course. Instructor: Staff
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ARTSVIS 269S - Special Topics in Visual Arts
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ARTSVIS 54: Introduction to Visual Practice
1.00 Credits
Duke University
Basic principles and methods of visual practice: 2DD and 3DD composition, drawing, color theory, photographic and architectural principles, as well as digital and time-based media like film, video, and performance. Visuality in everyday life and its impact on other fields of knowledge. Includes methods such as mapping, virtual environments, graph theory, and vernacular visual practices. Intended primarily for first and second year students. Prerequisite for all intermediate and advanced Visual Arts and Visual Practice classes. Instructor: Lasch
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ARTSVIS 60: Duke-Administered Study Abroad: Special Projects in Visual Arts
1.00 Credits
Duke University
Projects differ by section. Instructor: Staff
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ARTSVIS 60 - Duke-Administered Study Abroad: Special Projects in Visual Arts
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ARTSVIS 81FCS: Topics in Visual Arts
1.00 Credits
Duke University
Subjects, areas, or themes that embrace art and visual culture. Open only to students in the Focus program. Instructor: Staff
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ARTSVIS 81FCS - Topics in Visual Arts
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ARTSVIS 87FCS: Visual Representation and Visual Culture
1.00 Credits
Duke University
Understanding of human perception, visualization, and computer graphics techniques. Basic principles of perception like lightness, brightness, contrast, constancy, color theory, and visual attention. Use of current visualization techniques in graph tools, volume rendering, surface rendering, use of glyphs, and animation to see their strengths, weaknesses, and visual artifacts. Lectures and readings on theoretical foundation of particular techniques. Course projects on the practical application of techniques to real-world datasets. Represent student data in several ways and determine the best method. Open only to students in the Focus Program. Instructor: Brady
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ARTSVIS 87FCS - Visual Representation and Visual Culture
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BA (551): Introduction to Finance (BA 551) ** available as of 05/01/2011
3.00 Credits
Duke University
This course introduces students to a wide range of research topics in empirical corporate finance. Topics covered are related to capital structure, compensation, corporate tax effects, entrepreneurship, financial intermediation, governance, mergers and acquisitions, payout, and venture capital (though, not every topic is covered every year). The course is intended for Ph.D. students. Masters or other students must have 1) already taken Finance I and received an ¿A¿, and 2) request instructor permission to take this course. Instructor: Staff
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BA (551) - Introduction to Finance (BA 551) ** available as of 05/01/2011
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BA 215: Risk Management
3.00 Credits
Duke University
No course description available.
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BA 215 - Risk Management
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BA 501: Game Theory
3.00 Credits
Duke University
Basic topics in noncooperative game theory: representations of games in normal and extensive form and solution concepts, including Nash equilibrium, subgame perfect Nash equilibrium, perfect Bayesian equilibrium, sequential equilibrium, perfect equilibrium, proper equilibrium, correlated equilibrium, iterated dominance, and rationalizationibility. Discussion of the relation between the normal and extensive form and the relations among the various solution concepts. Application of interest to the students covered as time permits. Instructor: Marx
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BA 510: Bayesian Inference and Decision
3.00 Credits
Duke University
Methods of Bayesian inference and statistical decision theory, with emphasis on the general approach of modeling inferential and decision-making problems as well as the development of specific procedures for certain classes of problems. Topics include subjective probability, Bayesian inference and prediction, natural-conjugate families of distributions, Bayesian analysis for various processes, Bayesian estimation and hypothesis testing, comparisons with classical methods, decision-making criteria, utility theory, value of information, and sequential decision making. Instructor: Winkler
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BA 510 - Bayesian Inference and Decision
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