  Machine-learning (ML) algorithms will play a crucial role in studying the
large datasets delivered by new facilities over the next decade and beyond.
Here, we investigate the capabilities and limits of such methods in finding
galaxies with brightness-variable active galactic nuclei (AGN). Specifically,
we focus on an unsupervised method based on self-organizing maps (SOM) that we
apply to a set of nonparametric variability estimators. This technique allows
us to maintain domain knowledge and systematics control while using all the
advantages of ML. Using simulated light curves that match the noise properties
of observations, we verify the potential of this algorithm in identifying
variable light curves. We then apply our method to a sample of ~8300 WISE
color-selected AGN candidates in Stripe 82, in which we have identified
variable light curves by visual inspection. We find that with ML we can
identify these variable classified AGN with a purity of 86% and a completeness
of 66%, a performance that is comparable to that of more commonly used
supervised deep-learning neural networks. The advantage of the SOM framework is
that it enables not only a robust identification of variable light curves in a
given dataset, but it is also a tool to investigate correlations between
physical parameters in multi-dimensional space - such as the link between AGN
variability and the properties of their host galaxies. Finally, we note that
our method can be applied to any time-sampled light curve (e.g., supernovae,
exoplanets, pulsars, and other transient events).
