PICO is a dark matter experiment using superheated bubble chamber technology. One of the main analysis challenges in PICO is to unambiguously distinguish between background events and nuclear recoil events from possible WIMP scatters. The conventional discriminator, acoustic parameter (AP), utilizes frequency analysis in Fourier space to compute the acoustic power, which is proven to be different for alpha and nuclear recoils. In a recent machine learning development, an intern collaborator demonstrated extremely powerful discriminators using semi-supervised learning. I will be presenting the results he achieved, and provide an outlook for machine learning in future analysis.