This lecture gives an introduction to modeling optimization problems where parameters of the problem are uncertain. The primary focus will be on the case when the uncertain parameters are modeled as random variables. We will introduce both two-stage, recourse-based stochastic programming and chance-constrained approaches. Statistics that measure the value of computing a solution to the stochastic problem will be introduced. We will show how to create an equivalent "extensive form" formulations of the instances, so that they may be solved with standard optimization software.