The second part of this two-part lecture series will discuss recent advances in the integration of geometrical and topological aspects into deep learning models. Starting from hybrid models for graph learning tasks, we will subsequently leave traditional contemporary machine learning methods behind and present a new class of models that are specifically poised to leverage intrinsic geometrical-topological properties of data.