This course covers state-of-the-art machine learning techniques for robotic and industrial applications. Topics include:
-Recap of machine learning techniques used in the course
-Learning of redundant manipulator kinematics and dynamics
-Learning of motion primitives for motion generation
-Reinforcement learning
-Policy improvement by stochastic optimization
-Industrial applications of machine learning
The course is held in a one-week block in the semester break, probably in February. The exact dates will be negotiated with the participants and announced on this website. The morning sessions follow a lecture format. In the afternoon, we have hands-on programming sessions in the GZI computer pool. Lectures and material are in English.
Interested students, please add the course to your personal schedule in the eKVV for communication purposes.
The course is open to Bachelor, Master and PhD students. There is no special background required for attendance. Having attended any machine learning or robotics lecture, as well as experience with Matlab is helpful.
Rhythmus | Tag | Uhrzeit | Format / Ort | Zeitraum |
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Modul | Veranstaltung | Leistungen | |
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39-Inf-EGMI Ergänzungsmodul Informatik | vertiefendes Seminar 1 | unbenotete Prüfungsleistung
|
Studieninformation |
vertiefendes Seminar 2 | unbenotete Prüfungsleistung
|
Studieninformation | |
vertiefendes Seminar 3 | unbenotete Prüfungsleistung
|
Studieninformation | |
vertiefendes Seminar 4 | unbenotete Prüfungsleistung
|
Studieninformation | |
39-M-Inf-MIKE Modularisierter individueller Kompetenz-Erwerb (MiKE) | - | unbenotete Prüfungsleistung | Studieninformation |
Die verbindlichen Modulbeschreibungen enthalten weitere Informationen, auch zu den "Leistungen" und ihren Anforderungen. Sind mehrere "Leistungsformen" möglich, entscheiden die jeweiligen Lehrenden darüber.
Zu dieser Veranstaltung existiert ein Lernraum im E-Learning System. Lehrende können dort Materialien zu dieser Lehrveranstaltung bereitstellen: