Area of specialization Robotics
Contact:
R. Siegwart
Please comment on your experience with these courses
below under the corresponding headings. Always specify the semester and the name of the lecturer. In particular mention what you have perceived as prerequisites and dependencies.
General Remarks
Specific Remarks on Courses
Theory of Robotics and Mechatronics
Autonomous Mobile Robots
Probabilistic Artificial Intelligence
Deep Learning
- Took the class in 2019 (Winter) under Prof. Hofmann, when the class was worth 5 credits. The class gives an overview of the field, which means it can omit some details about specific applications/architectures. You can use the (mandatory) project as a way to get familiar with a framework like PyTorch/TensorFlow. The exam was not difficult, and it wasn't necessary at all to solve every question to pass.
Prerequisite: Some Machine Learning (e.g. Intro to ML should be enough) would be advised.
The class can be a good starting point. Could be combined with AML (which dives a bit deeper in "classical" ML), or with Comp. Vision, or projects in Finance, etc.
Computer Vision
Image Analysis and Computer Vision
Dynamic Programming and Optimal Control
Autumn semester 2021: (R. D'Andrea)
This is a stand-alone course about dynamic programming algorithms and (stochastic) shortest path problems. Some very basics in stochastic are needed but those will also get introduced in the very beginning of the course. It's an interesting course but the exam isn't computer based and you have to perform the dynamic programming algorithm by hand. But this isn't too hard and there is an additional programming exercise in Matlab where you can include the algorithm.
Recursive Estimation
Robot Dynamics
Introduction to Machine Learning
Advanced Machine Learning
Soft and Biohybrid Robotics
3D Vision
