Many real world problems in computer vision, robotics, computational neuroscience and natural language processing require to reason about highly uncertain, structured data, and draw global insight from local observations. Probabilistic graphical models (PGMs) allow addressing these challenges in a unified framework. This framework, which spans methods such as Bayesian networks and Markov random fields, uses ideas from discrete data structures in computer science to efficiently encode and manipulate probability distributions over high-dimensional spaces. These methods have been used in an enormous range of application domains, which include: image denoising, medical diagnosis, scene understanding, object recognition, speech recognition, natural language processing, robot navigation, and many more.
In this course, we will learn the basics of the PGM representation and how to construct them, study the problem of learning such models from data and performing inference, and use these models for making decisions. The course covers both the theoretical part of the PGMs and practical skills. The course is designed for graduate students. In order to reflect the internationalization of Leibniz Universität Hannover, this course will be offered in English.
