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- Modern C++ for Computer Vision
- 3D Coordinate Systems
- Photogrammetry I
- Mobile Sensing and Robotics I
- Photogrammetry II
- Mobile Sensing and Robotics II
- Techniques for Self-Driving Cars
- Master Project
While deep learning has achieved remarkable success in supervised and reinforcement learning problems, such as image classification, speech recognition, and game playing, these models are, to a large degree, specialized for the single task they are trained for. This course will cover the setting where there are multiple tasks to be solved, and study how the structure arising from multiple tasks can be leveraged to learn more efficiently or effectively. This includes:
- goal-conditioned reinforcement learning techniques that leverage the structure of the provided goal space to learn many tasks significantly faster
- meta-learning methods that aim to learn efficient learning algorithms that can learn new tasks quickly
- curriculum and lifelong learning, where the problem requires learning a sequence of tasks, leveraging their shared structure to enable knowledge transfer
This is a graduate-level course. By the end of the course, students will be able to understand and implement the state-of-the-art multi-task learning and meta-learning algorithms and be ready to conduct research on these topics.
D. Traum, und U. Geneve. In Susann LuperFoy, Anton Nijhholt, and Gert Veldhuijzen van Zanten, editors, Proceedings of Twente Workshop on Language Technology, TWLT-II, Seite 1--11. (1996)
F. R.. (2011)cited By (since 1996) 0; Conference of 2011 ACM Special Interest Group for Information Technology Education Conference, SIGITE'11; Conference Date: 20 October 2011 through 22 October 2011; Conference Code: 87347.
M. Masuch, und L. Nacke. International Conference on Computer Games: Artificial Intelligence, Design and Education, Seite 347--351. Reading, UK, University of Wolverhampton UK, (November 2004)