Introduction to Deep Learning with Applications to Stochastic Control and Games

Explore the fundamentals of deep learning and its applications in stochastic control and game theory in this comprehensive lecture by Ruimeng Hu from the University of California, Santa Barbara. Delivered as part of the Fields-CFI Bootcamp on Machine Learning for Quantitative Finance, this nearly two-hour presentation delves into the intersection of artificial intelligence, financial modeling, and decision-making under uncertainty. Gain insights into how deep learning techniques can be applied to solve complex problems in quantitative finance and game theory, providing a solid foundation for further study in these cutting-edge fields.

Introduction to deep learning with applications to stochastic control and games