Stochastic Optimization and Reinforcement Learning

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.