Computer Science

This course provides an overview of the theory of and practically useful methods in computer vision, with applications within e.g. vision systems, non-invasive measurements, and augmented reality. Participants develop problem solving skills, with and without a computer, using mathematical tools taken from many areas of the mathematical sciences, in particular geometry, matrix theory, algebraic geometry, optimization, mathematical statistics, invariant theory and transform theory. Course topics include projective geometry, geometric transformations, modelling of cameras. camera calibration. epipolar geometry. stereo vision, photogrammetry. model fitting., robust metrics, minimal solvers, 3D-modelling. geometry of surfaces and their silhouettes. deformable models, and visualization. Assumed prior knowledge: FMAF05 Systems and Transforms, or equivalent (for example FMAF10).

This course teaches students to model and analyze typical mechatronic devices and their implementation using digital computers, with particular emphasis on robotic systems. Students develop kinematics and dynamic models of robots and examine the electro-mechanical design aspects of mechatronic systems. The course investigates intelligent methods for robotic navigation as well as trajectory and path planning.

This course examines the fundamental techniques of some significant approaches within Artificial Intelligence (AI) for the solution of difficult problems. In particular, the course discusses local research techniques in a space of solutions, systems with constraints, soft constraints, planning techniques, representation and manipulation of knowledge with and without uncertainty, decision theory, reasoning techniques with preferences, and aggregation of preferences in a multi-agent context. The structure and the topics of the course is as follows: problem resolution, and local search algorithms; constraint-based systems and soft constraints; preference reasoning and preference aggregation in multi-agent systems; decision theory; treatment of uncertainty and probabilistic reasoning; planning; and artificial intelligence in society. The course recommends students have basic knowledge of programming and algorithms as a prerequisite.

This course examines the principles, mathematical models and applications of computer vision. Topics include: image processing techniques, feature extraction techniques, imaging models and camera calibration techniques, stereo vision, and motion analysis.

This course explores both theoretical and practical aspects of cryptography, authentication, and information security. Students learn the relevant mathematical techniques associated with cryptography, the principles of cryptographic techniques and how to perform implementations of selected algorithms in this area, and explore the application of security techniques in solving real-life security problems in practical systems.

This course provides the practical tools for developing, applying, and investigating machine learning methods in Python. The course utilizes libraries including Pandas, PyTorch, JAX, and Cython.

In this hands-on course, students work in interdisciplinary teams to uncover the rich history of Utrecht and share findings with the public. Combining historical, architectural, and societal data, students develop and design an innovative application for the city of Utrecht. In the process, students cooperate across disciplinary borders, take charge of their own learning process, and experimentally assess the added value of new media and ICT. The course accumulates in presentations and interactive demos of the teams’ final prototypes.