While deterministic optimization problems are very useful in practice, often times the assumption that all data is known in advance does not hold true. One possible way to relax this assumption is to assume that the data depends on random variables. This assumption leads to stochastic optimization problems. In this talk, I will talk about a few different approaches on how to deal with stochastic optimization problems, with the goal to give an initial overview of several different issues that arise in these problems.