Topological Methods in Machine Learning: A Tutorial for Practitioners

TL;DR

This tutorial introduces practitioners to topological machine learning techniques like persistent homology and Mapper, demonstrating their practical applications in analyzing complex data structures beyond traditional methods.

Contribution

It provides a comprehensive, hands-on introduction to key topological methods in machine learning, with practical examples and case studies for real-world application.

Findings

Persistent homology captures multi-scale topological features.

Mapper creates interpretable high-dimensional data summaries.

The tutorial enables practical application of TML techniques.

Abstract

Topological Machine Learning (TML) is an emerging field that leverages techniques from algebraic topology to analyze complex data structures in ways that traditional machine learning methods may not capture. This tutorial provides a comprehensive introduction to two key TML techniques, persistent homology and the Mapper algorithm, with an emphasis on practical applications. Persistent homology captures multi-scale topological features such as clusters, loops, and voids, while the Mapper algorithm creates an interpretable graph summarizing high-dimensional data. To enhance accessibility, we adopt a data-centric approach, enabling readers to gain hands-on experience applying these techniques to relevant tasks. We provide step-by-step explanations, implementations, hands-on examples, and case studies to demonstrate how these tools can be applied to real-world problems. The goal is to equip researchers and practitioners with the knowledge and resources to incorporate TML into their work, revealing insights often hidden from conventional machine learning methods. The tutorial code is available at https://github.com/cakcora/TopologyForML