Neural SDEs, Deep Learning and Stochastic Control

Neural SDEs, Deep Learning and Stochastic Control

Explore the intersection of neural stochastic differential equations, deep learning, and stochastic control in this lecture by Lukasz Szpruch from the Alan Turing Institute and University of Edinburgh. Delve into modeling techniques in finance, comparing classical models with modern approaches like optimal transport and neural networks. Learn about calibration algorithms, simulations, and extensions incorporating additional market information. Examine the connections between neural SDEs, stochastic control, and gradient flows, with practical examples including stochastic gradient descent. Gain insights into cutting-edge quantitative finance techniques as part of the Fields-CFI Bootcamp on Machine Learning for Quantitative Finance.