Computational Topology and Topological Data Analysis

This is a course for PhD students. Master students are also allowed and encouraged to participate. The main topic of the course is to introduce computational aspects in applied topology, computational topology, in order to provide the main mathematical ingredients to the the rather new field of research called Topological Data Analysis - TDA.

TDA aims at capturing the shape of data as a robust and multiscale descriptor to be integrated in data handling: classification, comparison, multidimensional reduction, etc ...

First, we deal with the constructions from data to simplicial complexes according to the kind of data: filtrations of data, point clouds, networks, and topological spaces. For each construction, we underline the possible dependence on a fixed scale parameter. Secondly, we introduce the necessary algebraic structures capturing topological informations out of a simplicial complex at a fixed scale, namely the simplicial homology groups. The so-obtained linear structures are then integrated into the multiscale framework of persistent homology where the entire persistence information is encoded in algebraic terms and the most advantageous persistence summaries available in the literature are discussed. Finally, we introduce the necessary metrics in order to state properties of stability of the introduced multiscale summaries under perturbations of input data. At the end, we give an overview of applications of persistent homology as well as a review of the existing tools in the broader area of Topological Data Analysis (TDA).