CMPS 290A
Examines the use of probability theory both in the design and analysis of algorithms. Uses probability theory to analyze the average performance of deterministic algorithms on randomly chosen or "typical" inputs, rather than on worst case inputs. Also a look at algorithms that use randomization, such as random walk and simulated annealing techniques. Examples of specific topics include martingales, random graphs, and rapidly mixing Markov Chains. Enrollment restricted to graduate students. Enrollment limited to 15. Offered in alternate academic years. May be repeated for credit. D. Haussler
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