April 20 at 4 pm (CEST).
Presenters: Nino Antulov-Fantulin (ETH Zurich)
Title: Complexity and Machine Learning with Financial applications
Abstract: Complexity science studies systems and problems that are composed of many components that may interact with each other in a dynamic and non-linear way. In this first part of the talk, the author will motivate and introduce several research questions and directions at the interface of complexity and machine learning: (i) the ability of neural networks to steer or control trajectories of network dynamical systems, (ii) node embedding of directed graphs and (iii) efficient Monte Carlo sampling of epidemic processes. In the second part of the talk, the author will focus on machine learning modelling for (crypto)financial markets.