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Course Criteria
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3.00 Credits
No course description available.
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1.00 - 3.00 Credits
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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3.00 Credits
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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3.00 Credits
No course description available.
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3.00 Credits
Not open to students who have taken Computer Science 325. Instructor: Staff
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1.00 Credits
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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3.00 Credits
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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1.00 - 3.00 Credits
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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3.00 Credits
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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1.00 - 6.00 Credits
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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