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METU | Course Syllabus
### Course Learning Outcomes

- Basics of linear stochastic systems that evolve in a Markovian fashion
- Details of finite state Markov Chains and controlled Markov chains
- Dynamic programming to be used in the Markov Decision Processes
- Markov Decision Processes restricted to the finite state Markov chain case
- Use of dynamic programming in Markov Decision Processes
- Meaning of partial information
- Kalman filtering as a state estimation tool for linear Gaussian systems
- Solution of the LQG problem as a stochastic partial observation optimal control problem