  Financial markets and more generally macro-economic models involve a large
number of individuals interacting through variables such as prices resulting
from the aggregate behavior of all the agents. Mean field games have been
introduced to study Nash equilibria for such problems in the limit when the
number of players is infinite. The theory has been extensively developed in the
past decade, using both analytical and probabilistic tools, and a wide range of
applications have been discovered, from economics to crowd motion. More
recently the interaction with machine learning has attracted a growing
interest. This aspect is particularly relevant to solve very large games with
complex structures, in high dimension or with common sources of randomness. In
this chapter, we review the literature on the interplay between mean field
games and deep learning, with a focus on three families of methods. A special
emphasis is given to financial applications.
