I currently teach the three courses mentioned above, broadly spanning my research areas. Below you can find further details of these courses.
Bayesian Estimation for Flight Robotics (Lecture + Practical)
Format: 24 Lectures (12 weeks) + 24 Practical Sessions
Occurrence: Offered every summer semester
Course Level: Master Students (Typically in their 1st or 2nd semester)
State estimation in autonomous UAVs, or flying robots, is a critical functionality. It allows a UAV to accurately localize itself and estimate the positions of a tracked target (from the UAV). Without robust state estimation, any of the other functionalities like planning, decision making and even low-level control can be jeopardized. The goal of this course is to provide students with a deep understanding of Bayesian estimation techniques and how they can be applied for UAV state estimation and target tracking. Students should be able to appreciate parametric ad non-parametric forms of estimation and their pros and cons. Finally, they should be able to implement various Bayesian filters for online applications, both in simulation and in a real UAV.
Flight Robotics Seminar (Seminar + Practical)
Format: 12 sessions (12 weeks) including seminars, tutorials, UAV building sessions, student presentations and a final flight demonstration
Occurrence: Offered every semester (summer and winter)
Course Level: Bachelor Students (Typically in their 4th or 5th semester)
The goal of this course is to give Bachelor students their first hands-on experience in aerial robotics. They will learn how to design, implement and analyse controllers and estimators. Moreover, they will learn how to design and build their own quadcopter robot and measure its system parameters. Additionally, the students will be introduced to the ROS programming environment, Gazebo and Matlab as well as the interfaces between them. The students will also receive training on how to use a motion capture system and fly their vehicle in a hall instrumented with such a system. Grading will be done based on a homework assignments and a presentation, which students have to prepare in groups of 1-2, on a topic such as attitude control, state estimation, etc.
Applied Machine Learning for Engineers (Lecture + Project)
Format: 24 Lectures (12 weeks) + 24 Tutorials Sessions.
Occurrence: Offered every winter semester
Course Level: Master Students (Typically in their 3rd or 4th semester)
In this course, Machine Learning algorithms are applied to problems arising in aerospace engineering. Lecture topics include:
Main tools used are Python, scikit-learn, PyTorch and TensorFlow (Keras).