Courses are together with exams the building blocks for modules. Please keep in mind that information on the contents, learning outcomes and, especially examination conditions are given on the module level only – see section "Assignment to Modules" above.
||The seminar focuses on variational inference methods that are applicable to robotic
perception and control problems. In recent years, applying machine learning to sequential
data of systems has developed as a promising path to the application of control methods to
very complex systems, such as robotic systems perceived through noisy sensors.
One promising direction is that of variational inference, where high-dimensional and hence
intractable problems of Bayesian inference can be approximated through stochastic
optimization techniques. Here, the member of a family of distributions closest to the true
posterior distribution is determined. This approximate distribution can then be used in place
of the true one, yielding solutions to learning, inference or control problems such as system
identification, state estimation, manipulation, localization or navigation.
In this course, students will familiarize themselves with the methodology of variational
inference in a deep learning framework. For that, fundamental knowledge such as machine
learning, control, and robotics will be refreshed. Typical problems of robotics will be phrased
as probabilistic inference and solutions based on recent developments will be studied and
Examples are Dreamer, Learning To Fly and variational inference-based SLAM: