This project focuses on the two important aspects of all modern statistical learning applications: robustness and generalisation. We are going to study how today's over-parameterized models (neural networks) can be trained effectively depending on the stability and convergence rates of the optimization procedures, model architecture, and objective formulation. With special focus, we are going to develop and test different probabilistic approaches to machine learning.