This document serves as support for a 2h class on optimization under uncertainty. It starts with a unified formulation of problems under uncertainty, setting them in the augmented space of the controlled and random parameters. This formulation encompasses robust objectives and reliability constraints. Then the issue of the double loop, that of optimization and estimation of risk measures is presented. Finally, a series of methods to approach such problems is described with an emphasis on those involving Gaussian processes.