  Astronomy is presently experiencing profound growth in the deployment of
machine learning to explore large datasets. However, transient quasi-periodic
oscillations (QPOs) which appear in power density spectra of many X-ray binary
system observations are an intriguing phenomena heretofore not explored with
machine learning. In light of this, we propose and experiment with novel
methodologies for predicting the presence and properties of QPOs to make the
first ever detections and characterizations of QPOs with machine learning
models. We base our findings on raw energy spectra and processed features
derived from energy spectra using an abundance of data from the NICER and RXTE
space telescope archives for two black hole low mass X-ray binary sources, GRS
1915+105 and MAXI J1535-571. We advance these non-traditional methods as a
foundation for using machine learning to discover global inter-object
generalizations between - and provide unique insights about - energy and timing
phenomena to assist with the ongoing challenge of unambiguously understanding
the nature and origin of QPOs. Additionally, we have developed a publicly
available Python machine learning library, QPOML, to enable further Machine
Learning aided investigations into QPOs.
