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COMPSCI 326: Systems Modeling
3.00 Credits
Duke University
No course description available.
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COMPSCI 326 - Systems Modeling
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COMPSCI 327: Seminar in Computer Systems Analysis
1.00 - 3.00 Credits
Duke University
Topics in computer systems analysis, especially for fault-tolerant systems, including reliability, availability and performance analysis, comparative analysis of architectures, performability, analytic and numerical solution techniques, stochastic Petri nets, simulation. Not open to students who have taken Computer Science 381. 1 to 3 units. Instructor: Trivedi
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COMPSCI 327 - Seminar in Computer Systems Analysis
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COMPSCI 331: Operating Systems Theory
3.00 Credits
Duke University
Advanced study of theoretical aspects of operating systems emphasizing models and control of concurrent processes, processor scheduling, and memory management. Prerequisite: Computer Science 226 and 231. Instructor: Ellis or Wagner
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COMPSCI 331 - Operating Systems Theory
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COMPSCI 337: VLSI Algorithmics
3.00 Credits
Duke University
No course description available.
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COMPSCI 337 - VLSI Algorithmics
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COMPSCI 340: Theory of Computation
3.00 Credits
Duke University
Not open to students who have taken Computer Science 325. Instructor: Staff
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COMPSCI 340 - Theory of Computation
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COMPSCI 355: Principles of Research Management
1.00 Credits
Duke University
A survey of topics in modern research management techniques that will cover proven successful principles and their application in the areas of research lab organization, resource management, organization of technical projects, team leadership, financial accountability, and professional ethics. Instructor: Staff
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COMPSCI 355 - Principles of Research Management
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COMPSCI 364: Advanced Topics in Nonlinear and Complex Systems
3.00 Credits
Duke University
Survey of current research topics that may include: advanced signal analysis (wavelets, Karhunen-Loeve decomposition, multifractals), bifurcation theory (amplitude and phase equations, symmetry breaking), spatio-temporal chaos, granular flows, broken ergodicity, complexity theory of dynamical systems, and adaptive systems (genetic algorithms, neural networks, artificial life). Emphasis on quantitative comparisons between theory, simulations, and experiments. Not open to students who have taken Computer Science 313. Prerequisite: Computer Science 264 or Physics 213; recommended: Physics 230, 203, or equivalent. Instructor: Greenside
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COMPSCI 364 - Advanced Topics in Nonlinear and Complex Systems
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COMPSCI 370: Seminar in Artificial Intelligence
1.00 - 3.00 Credits
Duke University
Topics in artificial intelligence, such as natural language understanding, learning, theorem proving and problem solving, search methodologies. Topics will vary from semester to semester. Includes research literature reading with student presentation. Not open to students who have taken Computer Science 382. Instructor: Staff
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COMPSCI 370 - Seminar in Artificial Intelligence
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COMPSCI 376: Advanced Topics in Artificial Intelligence
3.00 Credits
Duke University
Course content will vary from year to year and will include a detailed study of one or more of the following: mechanical theorem proving, natural language processing, automatic program synthesis, machine learning and inference, representations of knowledge, languages for artificial intelligence research, artificial sensorimotor systems, and others. Not open to students who have taken Computer Science 315. Prerequisite: Computer Science 270. Instructor: Biermann or Loveland
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COMPSCI 376 - Advanced Topics in Artificial Intelligence
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COMPSCI 395: Research
1.00 - 6.00 Credits
Duke University
Instruction in methods used in the investigation of original problems. Individual work and conferences. 1 to 6 units. Instructor: All members of the graduate staff
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COMPSCI 395 - Research
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