MATH 60623A

This course presents a unified framework for decision-making over a planning horizon in an uncertain environment, focusing on stochastic optimization techniques. Several classes of such problems are studied, together with the corresponding solution methods. Each of the classes of problems is motivated and illustrated using real applications of business analytics.

The course equips students with a diverse toolkit for tackling sequential decision-making problems under uncertainty using stochastic optimization. Students learn how to model these problems effectively and select the most appropriate solution strategies, ranging from classical optimization methods to cutting-edge techniques leveraging neural networks and reinforcement learning. The course requires a strong understanding of algorithmic thinking and computer programming.