  The purpose of this thesis is to convey the basic concepts of information
geometry and its applications to non-specialists and those in applied fields,
assuming only a first-year undergraduate background in calculus, linear
algebra, and probability theory / statistics. We first begin with an
introduction to the EM algorithm, providing a typical use case in Python,
before moving to an overview of basic Riemannian geometry. We then introduce
the core concepts of information geometry and the $em$ algorithm, with an
explicit calculation of both the $e$ and $m$ projection, before closing with a
discussion of an important application of this research to the field of deep
learning, providing a novel implementation in Python.
