392171 Machine Learning in Robotics (int. Track) (V+Ü) (WiSe 2016/2017)
Contents, comment
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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 learningThe 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.
Requirements for participation, required level
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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.
Teaching staff
Dates ( Calendar view )
| Frequency | Weekday | Time | Format / Place | Period | |
|---|---|---|---|---|---|
| block | Block | 9-13 | V2-200 | 06.-10.03.2017 |
Subject assignments
| Module | Course | Requirements | |
|---|---|---|---|
| 39-Inf-EGMI Ergänzungsmodul Informatik | vertiefendes Seminar 1 | Ungraded examination
|
Student information |
| vertiefendes Seminar 2 | Ungraded examination
|
Student information | |
| vertiefendes Seminar 3 | Ungraded examination
|
Student information | |
| vertiefendes Seminar 4 | Ungraded examination
|
Student information | |
| 39-M-Inf-MIKE Modularisierter individueller Kompetenz-Erwerb (MiKE) | - | Ungraded examination | Student information |
The binding module descriptions contain further information, including specifications on the "types of assignments" students need to complete. In cases where a module description mentions more than one kind of assignment, the respective member of the teaching staff will decide which task(s) they assign the students.
- E-Learning Space
- E-Learning Space
- Registered number: 20
- This is the number of students having stored the course in their timetable. In brackets, you see the number of users registered via guest accounts.
- Address:
- WS2016_392171@ekvv.uni-bielefeld.de
- This address can be used by teaching staff, their secretary's offices as well as the individuals in charge of course data maintenance to send emails to the course participants. IMPORTANT: All sent emails must be activated. Wait for the activation email and follow the instructions given there.
- If the reference number is used for several courses in the course of the semester, use the following alternative address to reach the participants of exactly this: VST_79717012@ekvv.uni-bielefeld.de
- Coverage:
- 5 Students to be reached directly via email
- Notes:
- Additional notes on the electronic mailing lists
- Email archive
- Number of entries 0
- Open email archive
- Last update basic details/teaching staff:
- Tuesday, June 28, 2016
- Last update times:
- Thursday, January 26, 2017
- Last update rooms:
- Thursday, January 26, 2017
- Type(s) / SWS (hours per week per semester)
- lecture (V) + exercise (Ü) / 2+2
- Language
- This lecture is taught in english
- Department
- Faculty of Technology
- Questions or corrections?
- Questions or correction requests for this course?
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- Clashing dates for this course
- Links to this course
- If you want to set links to this course page, please use one of the following links. Do not use the link shown in your browser!
- The following link includes the course ID and is always unique:
- https://ekvv.uni-bielefeld.de/kvv_publ/publ/vd?id=79717012
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- ID
- 79717012