392151 Probabilistic Learning for Visual Computing (S) (SoSe 2024)
Contents, comment
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This course offers a probabilistic perspective to machine learning, applied to visual computing applications. A central theme is the learning of neural scene representations from labeled and unlabeled data. The students will present recent papers on computer graphics and computer vision. Practical experiences for applying probabilistic models in pytorch are gained in the assignments and tutorials.
The course is largely based on the Visual AI course I gave at UBC: https://www.cs.ubc.ca/~rhodin/2022_2023_CPSC_533R/
Requirements for participation, required level
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excellent Python programming skills, fundamental mathematics, basics of machine learning or neural networks.
Teaching staff
Dates ( Calendar view )
| Frequency | Weekday | Time | Format / Place | Period | |
|---|---|---|---|---|---|
| weekly | Mi | 14-16 | X-E0-207 | 08.04.-19.07.2024
not on: 5/1/24 |
Subject assignments
| Module | Course | Requirements | |
|---|---|---|---|
| 39-M-Inf-AI-app Applied Artificial Intelligence | Applied Artificial Intelligence: Seminar | Student information | |
| - | Graded examination | Student information | |
| 39-M-Inf-AI-app-foc Applied Artificial Intelligence (focus) | Applied Artificial Intelligence (focus): Seminar | Student information | |
| - | Ungraded examination Graded examination | Student information | |
| 39-M-Inf-INT-app Applied Interaction Technology | Applied Interaction Technology: Seminar | Student information | |
| - | Graded examination | Student information | |
| 39-M-Inf-INT-app-foc Applied Interaction Technology (focus) | Applied Interaction Technology (focus): Seminar | Student information | |
| - | Ungraded examination Graded 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.
| Degree programme/academic programme | Validity | Variant | Subdivision | Status | Semester | LP | |
|---|---|---|---|---|---|---|---|
| Studieren ab 50 |
E-Learning Space
A corresponding course offer for this course already exists in the e-learning system. Teaching staff can store materials relating to teaching courses there:
- Registered number: 23 (1)
- 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:
- SS2024_392151@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_452304825@ekvv.uni-bielefeld.de
- Coverage:
- 22 Students to be reached directly via email
- Notes:
- Additional notes on the electronic mailing lists
- Last update basic details/teaching staff:
- Monday, February 26, 2024
- Last update times:
- Monday, February 5, 2024
- Last update rooms:
- Monday, February 5, 2024
- Type(s) / SWS (hours per week per semester)
- S / 2
- Language
- This lecture is taught in english
- Department
- Faculty of Technology
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- Questions or correction requests for this course?
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- Links to this course
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- https://ekvv.uni-bielefeld.de/kvv_publ/publ/vd?id=452304825
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- ID
- 452304825