The goal of this two-day workshop is to provide a detailed overview of machine learning techniques applied for finance. We offer insights into the latest techniques for modelling financial markets and focus on option pricing and calibration.

We not only tackle the theory but give practical guidance and live demonstrations of the computational methods involved. After introducing the subject we cover Gaussian Process Regression and Artificial Neural Networks and show how such methods can be applied to solve option pricing problems, speed up the calculation of xVAs or apply them for hedging.

We further show how to use existing pricing libraries to interact with machine learning environments often set up in Python.

We explain how to set up the methods mainly in Python using Keras, Tensorflow or SciKit Learn. We give many examples which are directly related to financial mathematics and can be explored further after the course. All the material is available as Jupyther notebooks. For Gaussian Processes we use Matlab and Python examples.

This workshop covers the fundamentals and it illustrates the application of state-of-the-art machine learning applications for application to Mathematical Finance.

The workshop is designed as a 2-day event.