  Astronomical observations already produce vast amounts of data through a new
generation of telescopes that cannot be analyzed manually. Next-generation
telescopes such as the Large Synoptic Survey Telescope and the Square Kilometer
Array are planned to become operational in this decade and the next, and will
increase the data volume by many orders of magnitude. The increased spatial,
temporal and spectral resolution afford a powerful magnifying lens on the
physical processes that underlie the data but, at the same time, generate
unprecedented complexity hard to exploit for knowledge extraction. It is
therefore imperative to develop machine intelligence, machine learning (ML) in
particular, suitable for processing the amount and variety of astronomical data
that will be collected, and capable of answering scientific questions based on
the data. Astronomical data exhibit the usual challenges associated with 'big
data' such as immense volumes, high dimensionality, missing or highly distorted
observations. In addition, astronomical data can exhibit large continuous
observational gaps, very low signal-to-noise ratio and the need to distinguish
between true missing data and non-detections due to upper limits). There are
strict laws of physics behind the data production which can be assimilated into
ML mechanisms to improve over general off-the-shelf state-of-the-art methods.
Significant progress in the face of these challenges can be achieved only via
the new discipline of Astroinformatics: a symbiosis of diverse disciplines,
such as ML, probabilistic modeling, astronomy and astrophysics, statistics,
distributed computing and natural computation. This editorial summarizes the
contents of a soon to appear Focus Issue of the PASP on Machine Learning in
Astronomy and Astrophysics (with contributions by 69 authors representing 15
countries, from 6 continents).
