Real world data is often only partially labeled. Because completely labeling data can be expensive or even impossible in some cases, a common scenario involves having only a small number of labeled samples from the class of interest, and a large quantity of unlabeled and unknown data. A classification boundary differentiating the underlying positive and negative classes is still desired. This is known as the Positive and Unlabeled learning problem, or PU learning, and is of growing importance in machine learning. Fortunately, PU learning algorithms exist that can create effective models using low power and memory requirements. In this talk, Ms. Jaskie will present several potential embedded applications for PU learning and describe how sensors, tiny ML, and PU learning all complement one another. In addition, she will describe low complexity solutions and explain why the techniques are so effective and in growing demand.