Astrophysics and cosmology are rich with data. The advent of wide-area
digital cameras on large aperture telescopes has led to ever more ambitious
surveys of the sky. Data volumes of entire surveys a decade ago can now be
acquired in a single night and real-time analysis is often desired. Thus,
modern astronomy requires big data know-how, in particular it demands highly
efficient machine learning and image analysis algorithms. But scalability is
not the only challenge: Astronomy applications touch several current machine
learning research questions, such as learning from biased data and dealing with
label and measurement noise. We argue that this makes astronomy a great domain
for computer science research, as it pushes the boundaries of data analysis. In
the following, we will present this exciting application area for data
scientists. We will focus on exemplary results, discuss main challenges, and
highlight some recent methodological advancements in machine learning and image
analysis triggered by astronomical applications
