Abstract:
I’ll give an introduction to the use of probabilistic models in machine learning and present the framework of graphical models. This framework is particularly useful for reasoning with uncertainty and facilitates the incorporation of a priori knowledge about the environment. In practice, many models are large-scale and approximate inference techniques are therefore required. I’ll give an overview of some of the basic techniques available and also discuss some of the potential applications of these models and associated techniques.