Topological Data Analysis (TDA) and Topological Machine Learning (TML) comprise a set of powerful techniques whose ability to extract robust geometric information has led to novel insights in the analysis of complex data.

Topology is concerned with understanding the global shape and structure of objects. When applied to data, topological methods provide a natural complement to conventional machine learning approaches, which tend to rely on local properties of the data. The main strengths of TDA/TML include their robustness to noise and ability to succinctly capture multi-scale behaviour.

Topological techniques have found great success in domains ranging from network science to drug and materials design, visualisation and dimensionality reduction, neuroscience, and time series analysis. Recently, connections between topology and deep learning have been explored, revealing promising avenues into regularization, interpretability, and protection from adversarial attacks.

This track will provide an accessible introduction to the use of topology in data science and machine learning. Via a selection of use cases from experts in the field, this track will showcase the benefits of integrating TDA and TML into traditional data science workflows.