Topological data analysis and machine learningTopological data analysis and machine learning Ανασκόπηση Review 2025-05-292023enTopological data analysis refers to approaches for systematically and reliably computing abstract ‘shapes’ of complex data sets. There are various applications of topological data analysis in life and data sciences, with growing interest among physicists. We present a concise review of applications of topological data analysis to physics and machine learning problems in physics including the unsupervised detection of phase transitions. We finish with a preview of anticipated directions for future research.This research is supported by the EU HORIZON—Project 101080085 — QCFD.http://creativecommons.org/licenses/by/4.0/Advances in Physics: X81Leykam_et_al_Adv. Phys. X_8(1)_2023.pdfChania [Greece]Library of TUC2025-05-29application/pdf2.2 MBfree Leykam Daniel Angelakis Dimitrios Αγγελακης Δημητριος Taylor & Francis Machine learning Strongly correlated quantum systems Persistent homology Phase transition Quantum computing Condensed matter physics Topological phase