Topological Data Analysis for Machine Learning

The goal of this tutorial is to give an introduction to the nascent field of topological machine learning, with the express purpose of being accessible to all audiences. The tutorial will discuss state-of-the art algorithms and provide the audience with the necessary competences to apply such techniques to their own research problems.


This tutorial targets machine leaning researchers of any background. It is helpful if you already have some some knowledge about concepts from undergraduate mathematics, such as vector spaces and groups. If not, do not worry–I will provide intuitive descriptions for all of them during the tutorial.

To make the most of this session, I would recommend the following things:

  • Some way of taking notes; the slides will be available¬†(see below), so you could also annotate them along the way.
  • A laptop with either Linux or Mac OS X will be helpful for in order to try out some of the package for computing persistent homology. This is not a necessity, though.

Course materials