Computer Science

In this course, students use probability theory to model uncertainty; design simple probabilistic models that facilitate prediction; conduct sound scientific analysis of data, and study the mathematical foundations of probabilistic modelling with Markov chains and simulation.

This course focuses on mobile robotics, emphasizing practical algorithms for navigation, all based around real hardware and tested in the real world. Key elements are: wheeled locomotion, motor control, and motion calibration; outward-looking sensors for behavioral control loops; probabilistic localization using particle filtering; advanced use of sensors for place recognition, occupancy mapping and planning; and an introduction to Simultaneous Localization and Mapping. The course is intensively practical, and all the key methods students learn are tested on robots they build.

This course provides an introduction to the foundations of 3D computer graphics. 

Students learn the basic methods used to define shapes, materials, and lighting when creating computer-generated images for use in film, games, and other applications. Topics include affine and projective transformations, clipping and windowing, visual perception, scene modeling and animation, algorithms for visible surface determination, reflection models, illumination algorithms, and color theory in depth. 

No official prerequisites, but the course assumes some programming experience in C or C++ and a basic knowledge of linear algebra. Exposure to calculus and image processing is useful but not required. 

The course's goal is to enable participants to acquire and process digital images in technical applications in a context-aware manner. The course introduces the basics of digital image processing, the acquisition of images in computing environments, and the extraction of semantic contents from the images. The goal of the course is the exemplary coverage of an interdisciplinary breadth, not necessarily an in-depth treatment of a specific domain. Fundamentals like sensor calibration, feature detection (e.g. edge extraction), matching and classification are taught. Integrated practical exercises cover operating a camera from a single-board computer and using a smartphone camera in a computer vision setting. Furthermore, exemplary machine learning approaches are used for “understanding” the images acquired previously. Software to be developed make use of the OpenCV Python library.