Praktikum: Data Analytics for Cyber-Physical Systems: Automatic Failure Diagnosis (IN0012, IN2106, IN4285)
Course 0000003018 in WS 2020/1
General Data
| Course Type | practical training |
|---|---|
| Semester Weekly Hours | 2 SWS |
| Organisational Unit | Informatics 4 - Chair of Software & Systems Engineering (Prof. Pretschner) |
| Lecturers |
Stephan Lipp Ehsan Zibaei Responsible/Coordination: Alexander Pretschner |
| Dates |
Wed, 15:00–17:00, MI 01.09.014 |
Assignment to Modules
-
IN2106: Master-Praktikum / Advanced Practical Course
This module is included in the following catalogs:- Further Modules from Other Disciplines
Further Information
Courses are together with exams the building blocks for modules. Please keep in mind that information on the contents, learning outcomes and, especially examination conditions are given on the module level only – see section "Assignment to Modules" above.
| additional remarks | As robotic systems are increasingly employed in different applications, more and more safety incidents occur during their operation. One way to improve the safety of robotic systems is to look into the vast amount of data produced by them and automatically learn the causes of the incidents. This way the next versions of the robotic system will be designed safer against similar faults. Causal discovery algorithms are powerful tools that use statistical dependency between variables to infer the causal structure and hence reveal the causes of the system failure. The focus of this practical course is on using causal inference methods to discover the causal relationship between events in the context of robotic systems in general and Unmanned Aerial Vehicles as a specific use case. |
|---|---|
| Links |
E-Learning course (e. g. Moodle) TUMonline entry |