  The goal of this tutorial is to introduce key models, algorithms, and open
questions related to the use of optimization methods for solving problems
arising in machine learning. It is written with an INFORMS audience in mind,
specifically those readers who are familiar with the basics of optimization
algorithms, but less familiar with machine learning. We begin by deriving a
formulation of a supervised learning problem and show how it leads to various
optimization problems, depending on the context and underlying assumptions. We
then discuss some of the distinctive features of these optimization problems,
focusing on the examples of logistic regression and the training of deep neural
networks. The latter half of the tutorial focuses on optimization algorithms,
first for convex logistic regression, for which we discuss the use of
first-order methods, the stochastic gradient method, variance reducing
stochastic methods, and second-order methods. Finally, we discuss how these
approaches can be employed to the training of deep neural networks, emphasizing
the difficulties that arise from the complex, nonconvex structure of these
models.
