Markov Chains and Decision-Making Processes

Brief course description This discipline involves the study of Markov chains, in which each element is completely determined by the previous one. These chains are widely used in the formulation of tasks of linking artificial intelligence to the behaviour of an agent in a certain environment, for example, a robot in a real environment, on which, for example, reinforcement learning is based. Expected learning outcomes Upon successful completion of the course, students will be able to: - work with methods of building probabilistic models describing stochastic dynamics of processes; - perform sampling and estimation; - develop a mass service system - be able to establish properties of solutions of stochastic systems.