In this talk we will begin by discussing some of the main concepts of machine learning at a high level. Then we will take a closer look at neural networks. And finally we will discuss an interesting application of free probability to deep learning observed by Pennington-Schoenholz-Ganguli.  The goal of this talk is to introduce mathematicians without much background in statistics to the fundamentals of machine learning while highlighting an interesting application of noncommutative probability.  Graduate students are highly encouraged to attend.