Stochastic programming is one of the fundamental approaches that can be used to model decision-making under uncertainty. It is concerned with the mathematical programming problems, where the uncertain problem parameters are represented by random variables, and it extends deterministic optimization by explicitly accounting for the uncertainty already in the modeling age. This course will provide a broad overview of the main themes and methods of the subject. This course covers various optimization models (chance-constrained optimization, two-stage stochastic programming models, optimization with risk measures, etc.), as well as their mathematical programming-based solution methods and applications to practical problems. Since stochastic programs are computationally challenging, there is a particular emphasis in this course on algorithmic tools (especially, on decomposition-based algorithms) for solving large-scale instances.