  In the era of large all-sky surveys, there will be a need for rapid,
automatic classifications of newly discovered transient objects. Our focus here
is the classification of supernovae (SNe). We consider random forest machine
learning algorithm applied to the classification of Type Ia and core-collapse
supernovae (CCSNe) by full light curves. We also quantitatively show the
potential of early-epoch classification using the same machine learning
techniques. The algorithm uses the shape and magnitude of the light curve peak
to determine the classification. This an initial study to essentially determine
SN type with as few data points as possible. New all-sky surveys will discover
new transients at a rapid rate and decisions will have to be made on very
little data which transients to follow-up. Here we present an initial study
where as few as five data points near peak, we an identify whether it is a Type
Ia or CCSNe with better than 80\% accuracy, but with several biases in the data
that will require addressing in future work.
