Learning with Structured Inputs and Outputs


Structured prediction methods have revolutionized the way in which researchers in computer vision and other application areas can tackle the task of predicting complex object with many interconnected parts. The lecture will give an in-depth introduction into the theory and applications of one of the currently most popular frameworks: discrete probabilistic graphical models. Using example from computer vision we will discuss prediction algorithms, such as belief propagation and graph-cuts, as well as methods for parameter learning based on classic maximum likelihood as well as the maximum-margin principle.