  Machine learning has emerged as a popular and powerful approach for solving
problems in astrophysics. We review applications of machine learning techniques
for the analysis of ground-based gravitational-wave detector data. Examples
include techniques for improving the sensitivity of Advanced LIGO and Advanced
Virgo gravitational-wave searches, methods for fast measurements of the
astrophysical parameters of gravitational-wave sources, and algorithms for
reduction and characterization of non-astrophysical detector noise. These
applications demonstrate how machine learning techniques may be harnessed to
enhance the science that is possible with current and future gravitational-wave
detectors.
