The goal of the seminar is to understand, implement and improve several novel techniques from machine learning in Finance. Concretely we shall teach a machine a calibration functional, i.e. how to map market data to appropriate models, or we teach a machine how to optimally hedge options given a pre-specified model for market prices, or how to solve non-linear PDEs, or how to filter signals from noisy observations possibly via rough paths methods. Prerequisities are some knowledge in mathematical Finance (pricing, hedging, optimal investment) and in Stochastic analysis, some understanding of neural networks, universal approximation theorems and network traning and some knowledge in implementation of machine-learning algorithms in Python. I recommend the following reading list and in particular the references on universal approximation in the first paper:
The first meeting for the seminar is on Wednesday, September 20, in my office in G floor at 4 pm.