This chapter describes applications of machine learning techniques, particularly supervised learning, to assist the solution of optimization problems, including warm-starting optimization solvers, identifying active/inactive inequality constraints and subsets of invariant variables, linearizing and convexifying nonconvex equations, and using machine learning models as surrogates of complex/unknown sets of equations.