Semi-supervised machine learning involves aspects of both supervised and unsupervised machine learning. A small portion of the training data is labeled, and this is used to classify the majority of the data, which is unlabeled. The goal here is to label several spectral features within the data and assess how well the model is able to pick up on the locations of these features through training in order to predict their location in new data.

This section will involve research that is not yet published. Come back again in a few months!