  Single-particle traces of the diffusive motion of molecules, cells, or
animals are by-now routinely measured, similar to stochastic records of stock
prices or weather data. Deciphering the stochastic mechanism behind the
recorded dynamics is vital in understanding the observed systems. Typically,
the task is to decipher the exact type of diffusion and/or to determine system
parameters. The tools used in this endeavor are currently revolutionized by
modern machine-learning techniques. In this Perspective we provide an overview
over recently introduced methods in machine-learning for diffusive time series,
most notably, those successfully competing in the
Anomalous-Diffusion-Challenge. As such methods are often criticized for their
lack of interpretability, we focus on means to include uncertainty estimates
and feature-based approaches, both improving interpretability and providing
concrete insight into the learning process of the machine. We expand the
discussion by examining predictions on different out-of-distribution data. We
also comment on expected future developments.
