ABSTRACT. This is a tutorial on machine learning for iterative methods researchers. The tutorial assumes little prior background in machine learning, but will be broad and move quickly. It will focus on key ideas in machine learning and mention the use of iterative methods when they arise (usually in optimization). It will also hint at PDEs and inverse problems. The first half of the tutorial will cover basic ideas including regularization, Bayesian inference, MCMC, Gaussian processes, and kernel methods. The second half of the tutorial will cover stochastic gradient descent, automatic differentiation, expectation maximization, variational inference, and online learning.