  Identifying phase transitions is one of the key challenges in quantum
many-body physics. Recently, machine learning methods have been shown to be an
alternative way of localising phase boundaries also from noisy and imperfect
data and without the knowledge of the order parameter. Here we apply different
unsupervised machine learning techniques including anomaly detection and
influence functions to experimental data from ultracold atoms. In this way we
obtain the topological phase diagram of the Haldane model in a completely
unbiased fashion. We show that the methods can successfully be applied to
experimental data at finite temperature and to data of Floquet systems, when
postprocessing the data to a single micromotion phase. Our work provides a
benchmark for unsupervised detection of new exotic phases in complex many-body
systems.
