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The goal of this course is to teach decision making under uncertainty. The course introduces the nature and basic concepts of stochastic processes first, and then teaches the following stochastic modeling techniques: Markov models, queuing models, dynamic programming, and Markov decision processes. The course also aims to familiarize students with the computer tools for solving decision making problems involving such models.
Decision making under uncertainty. Dynamic programming. Introduction to stochastic processes. Queuing models and applications. Markov chains and Markov processes. Game theory.
Upon succesful completion of this course, a student will be able to
1. Define stochastic processes (B1, a2)
2. Formulate an existing stochastic environment by using stochastic processes, Markov chains, or queuing models (B3, e)
3. Formulate and solve dynamic programming models (B3, e)
4. Evaluate the performance of a process, component or system and propose preferred solution alternatives based on sensitivity and scenario analysis (B6, e)
5. Apply software and programming knowledge to solve stochastic decision making problems (B3, k)