Please note that after Summer 2011, the Information Systems Management department was renamed Technology & Information Management. For classes after Summer 2011, please see the TIM schedule page.
Provides a systematic methodology and corresponding set of methods and analytical tools in stochastic models; reinforcement learning; stochastic (neuro-)dynamic programming; Bayesian graphical models; inference; and social networks used for web analytics and machine learning to achieve business intelligence (BI) and support research and applications in computer science, computer engineering, and electrical engineering, applied mathematics and statistics, business, management, and economics. Includes exposure to Hadoop for large-scale computation. Students should have solid background in probability equivalent to statistics, stochastic, methods, calculus, (and preferably) stochastic processes and optimization, or mathematical maturity and exposure to business intelligence and algorithms. (Formerly Information Systems and Technology Management 2.) Prerequisite(s): Computer Engineering 107 or Applied Mathematics and Statistics 131, or other undergraduate probability course recommended, or permission of instructor. Enrollment restricted to graduate students. AMS 205B, CMPE 230, and course ISM 250 recommended. R. Akella
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