Summary of Topological Methods in Machine Learning: a Tutorial For Practitioners, by Baris Coskunuzer et al.
Topological Methods in Machine Learning: A Tutorial for Practitioners
by Baris Coskunuzer, Cüneyt Gürcan Akçora
First submitted to arxiv on: 4 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computational Geometry (cs.CG); Algebraic Topology (math.AT)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A comprehensive introduction to Topological Machine Learning (TML) techniques, persistent homology and the Mapper algorithm, is presented in this tutorial. The paper focuses on practical applications of these methods, which analyze complex data structures that traditional machine learning may not capture. 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. The authors provide step-by-step explanations, implementations, hands-on examples, and case studies to demonstrate how these tools can be applied to real-world problems. TML has the potential to reveal insights often hidden from conventional machine learning methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Topological Machine Learning (TML) is a new way of analyzing complex data that uses ideas from algebraic topology. This tutorial teaches you two important TML techniques: persistent homology and the Mapper algorithm. These tools can help you find patterns in your data that you might not see with traditional machine learning methods. The authors provide examples and exercises so you can practice using these techniques to solve real-world problems. |
Keywords
* Artificial intelligence * Machine learning
