392221 Deep Learning (V) (SoSe 2024)
Contents, comment
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The lecture introduces the basic concepts of deep learning, covering the basic architectures, generative models, attacks and defebses, XAI methods, and structure processing models. Popular applications such as for image recognition will be discussed. On a practical side,mostly PyTorchwill be used.
Requirements for participation, required level
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programming skills, mathematics, basics of machine learning or neural networks
Teaching staff
Dates ( Calendar view )
| Frequency | Weekday | Time | Format / Place | Period | |
|---|---|---|---|---|---|
| weekly | Do | 14-16 | H5 | 08.04.-19.07.2024
not on: 5/9/24 / 5/30/24 / 7/18/24 |
Am 18.07.2024 findet die Klausur in H5 statt, siehe https://ekvv.uni-bielefeld.de/kvv_publ/publ/vd?id=462564444 |
Subject assignments
| Module | Course | Requirements | |
|---|---|---|---|
| 39-M-Inf-AI-adv-foc Advanced Artificial Intelligence (focus) | Advanced Artificial Intelligence (focus): Vorlesung | Graded examination
|
Student information |
| 39-M-Inf-AI-app Applied Artificial Intelligence | Applied Artificial Intelligence: Vorlesung | Student information | |
| - | Graded examination | Student information | |
| 39-M-Inf-AI-app-foc Applied Artificial Intelligence (focus) | Applied Artificial Intelligence (focus): Vorlesung | Student information | |
| - | Graded examination | Student information | |
| 39-M-Inf-DL Deep Learning | Deep Learning | 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.
Students are able distinguish different key architectures for deep learning, and they know the learning scenarios where these architectures can be used. The students know how to train a deep architecture, both, as regards the algorithmic pipeline and possible forms of implementation using modern systems.
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: 72 (2)
- 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_392221@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_452154377@ekvv.uni-bielefeld.de
- Coverage:
- 71 Students to be reached directly via email
- Notes:
- Additional notes on the electronic mailing lists
- Last update basic details/teaching staff:
- Friday, January 26, 2024
- Last update times:
- Friday, March 8, 2024
- Last update rooms:
- Friday, March 8, 2024
- Type(s) / SWS (hours per week per semester)
- V / 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
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- The following link includes the course ID and is always unique:
- https://ekvv.uni-bielefeld.de/kvv_publ/publ/vd?id=452154377
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- ID
- 452154377