Abstract
As population genomic datasets grow in size, researchers are faced with the daunting task of making sense of a flood of information. To keep pace with this explosion of data, computational methodologies for population genetic inference are rapidly being developed to best utilize genomic sequence data. In this review we discuss a new paradigm that has emerged in computational population genomics: that of supervised machine learning. We review the fundamentals of machine learning, discuss recent applications of supervised machine learning to population genetics that outperform competing methods, and describe promising future directions in this area. Ultimately, we argue that supervised machine learning is an important and underutilized tool that has considerable potential for the world of evolutionary genomics.
