Mini Course 1 We review methods for generating scenarios to approximate stochastic optimization problems. General methods such as Monte Carlo, Latin hypercube sampling and quasi-Monte Carlo methods will be discussed. We will provide an overview of properties of such methods, in terms of asymptotic convergence and behavior for finitely many samples. We will also review a number of specialized sequential sampling algorithms to solve stochastic optimization problems and methods to assess solution quality. In the context of multi-stage stochastic programs, we will pay particular attention to methods for generating scenario trees, such as moment-matching, clustering, and probability-based metrics. We will discuss applications of the methods studied in the course, especially in the areas of energy and finance. Material Homem-de-Mello, T. and Bayraksan, G., “Monte Carlo Sampling-Based Methods for Stochastic Optimization,” Surveys in Operations Research and Management Science, 19(1): 56–85, 2014 .