In this talk we review graph-based methods for machine learning. Topics include Laplace and Poisson learning and diffuse interface methods on graphs. We discuss the relationship between total variation minimization and graph cuts and show how to implement this in a variety of settings including semi-supervised learning, "active contours" on graphs, and modularity optimization on networks and for unsupervised learning.