  We present a novel Machine Learning (ML) based strategy to search for binary
black hole (BBH) mergers in data from ground-based gravitational wave (GW)
observatories. This is the first ML-based search that not only recovers all the
compact binary coalescences (CBCs) in the first GW transients catalog (GWTC-1),
but also makes a clean detection of GW151216 by only adding a new coincident
ranking statistic (MLStat) to a standard analysis that was used for GWTC-1. In
CBC searches, reducing contamination by terrestrial and instrumental
transients, which create a loud noise background by triggering numerous false
alarms, is crucial to improving the sensitivity for detecting true events. The
sheer volume of data and a large number of expected detections also prompts the
use of ML techniques. We perform transfer learning to train "InceptionV3", a
pre-trained deep neural network, along with curriculum learning to distinguish
GW signals from noisy events by analysing their continuous wavelet transform
(CWT) maps. MLStat incorporates information from this ML classifier into the
coincident search likelihood used by the standard PyCBC search. This leads to
at least an order of magnitude improvement in the inverse false-alarm-rate
(IFAR) for the previously "low significance" events GW151012, GW170729 and
GW151216. We also perform the parameter estimation of GW151216 using
SEOBNRv4HM_ROM. We carry out an injection study to show that MLStat brings
substantial improvement to the detection sensitivity of Advanced LIGO for all
compact binary coalescences. The average improvement in the sensitive volume is
~10% for low chirp masses (0.8-5 Msun), and ~30% for higher masses (5-50 Msun).
This work demonstrates the immense potential and readiness of MLStat for
finding new sources in current data and the possibility of its adaptation in
similar searches.
