Michael Jordan discusses two broad classes of statistical learning algorithms. One is probabilistic graphical models, a formalism that exploits the conjoined talents of graph theory and probability theory to build complex models out of simpler pieces, and the second is kernel methods, a formalism based on computationally-efficient representations of generalized notions of similarity in Hilbert spaces. From the Series:CSE Colloquia - 2004