Neural networks and Bayesian belief networks are learning and interface methods that have been developed in two widely separated research communities. The aim of the school is to bring together researchers from these two communities and study both types of networks as instances of a unified general graphical formalism. Courses will focus on probabilistic methods for learning in the graph formalism, with emphasis on analysis and algorithm design, theory and applications.