  The last decade has witnessed a rapid growth of the field of exoplanet
discovery and characterisation. However, several big challenges remain, many of
which could be addressed using machine learning methodology. For instance, the
most prolific method for detecting exoplanets and inferring several of their
characteristics, transit photometry, is very sensitive to the presence of
stellar spots. The current practice in the literature is to identify the
effects of spots visually and correct for them manually or discard the affected
data. This paper explores a first step towards fully automating the efficient
and precise derivation of transit depths from transit light curves in the
presence of stellar spots. The methods and results we present were obtained in
the context of the 1st Machine Learning Challenge organized for the European
Space Agency's upcoming Ariel mission. We first present the problem, the
simulated Ariel-like data and outline the Challenge while identifying best
practices for organizing similar challenges in the future. Finally, we present
the solutions obtained by the top-5 winning teams, provide their code and
discuss their implications. Successful solutions either construct highly
non-linear (w.r.t. the raw data) models with minimal preprocessing -deep neural
networks and ensemble methods- or amount to obtaining meaningful statistics
from the light curves, constructing linear models on which yields comparably
good predictive performance.
