  Black hole X-ray binary systems (BHBs) contain a close companion star
accreting onto a stellar-mass black hole. A typical BHB undergoes transient
outbursts during which it exhibits a sequence of long-lived spectral states,
each of which is relatively stable. GRS 1915+105 is a unique BHB that exhibits
an unequaled number and variety of distinct variability patterns in X-rays.
Many of these patterns contain unusual behaviour not seen in other sources.
These variability patterns have been sorted into different classes based on
count rate and color characteristics by Belloni et al (2000). In order to
remove human decision-making from the pattern-recognition process, we employ an
unsupervised machine learning algorithm called an auto-encoder to learn what
classifications are naturally distinct by allowing the algorithm to cluster
observations. We focus on observations taken by the Rossi X-ray Timing
Explorer's Proportional Counter Array. We find that the auto-encoder closely
groups observations together that are classified as similar under the Belloni
et al (2000) system, but that there is reasonable grounds for defining each
class as made up of components from 3 groups of distinct behaviour.
