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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.
This program aims to reunite researchers across disciplines that have played a role in developing the theory of reinforcement learning. It will review past developments and identify promising directions of research, with an emphasis on addressing existing open problems, ranging from the design of efficient, scalable algorithms for exploration to how to control learning and planning. It also aims to deepen the understanding of model-free vs. model-based learning and control, and the design of efficient methods to exploit structure and adapt to easier environments.
R. Sharipov. (2004)cite arxiv:math/0412421Comment: The textbook, AmSTeX, 132 pages, amsppt style, prepared for double side printing on letter size paper.
M. Walker, R. Passonneau, and J. Boland. Proceedings of the 39th Annual Meeting on Association for Computational Linguistics, page 515--522. Stroudsburg, PA, USA, Association for Computational Linguistics, (2001)
D. Traum, and U. Geneve. In Susann LuperFoy, Anton Nijhholt, and Gert Veldhuijzen van Zanten, editors, Proceedings of Twente Workshop on Language Technology, TWLT-II, page 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.