This course is designed to provide a basic understanding of dynamic decision making (DM) under uncertainty and related tools. It is expected that students will learn how to formulate DM problem and to solve it using the technique described. The course also introduces a basic of Fully Probabilistic Design (FPD), which is a non-trivial extension of Bayesian DM.  The course offers a unified view on stochastic filtration and dynamic programming, as well as a conceptually feasible construction of respective probabilistic description, and elements of multiple-participant DM.