392127 Deep Learning for Data Mining (V) (SoSe 2023)
Contents, comment
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The course will cover Large Language Models, Large Vision Models, Diffusion Models, and Transformers. This will include LLaMA, LLaVA, Stable Diffusion, BERT, ChatGPT, ViT, etc. These are neural network architectures that have become popular in recent years for their ability to handle large amounts of data and achieve state-of-the-art performance on a wide range of tasks. By the end of the course, you will have a strong theoretical foundation and be well-equipped to apply these techniques to real-world problems.
[1] Zoom: https://uni-bielefeld.zoom.us/j/67461280386?pwd=NUNRSjZHV1FicGlack1JWmJHTDFVZz09
Meeting ID: 674 6128 0386
Passcode: 123123
Teaching staff
Dates ( Calendar view )
| Frequency | Weekday | Time | Format / Place | Period |
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Subject assignments
| Module | Course | Requirements | |
|---|---|---|---|
| 39-M-Inf-VDM Vertiefung Datamining | Datamining II | 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.
Types of exams and conditions for credits:
Option 1: Oral exam with mark about the lecture topics. Successful oral exam yields 5 credits.
Option 2: Oral exam with questions about a mini-project. Successful mini-project report and oral exam with questions about the mini-project yields 5 credits.
- Registered number: 30
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- SS2023_392127@ekvv.uni-bielefeld.de
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- 24 Students to be reached directly via email
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- Last update basic details/teaching staff:
- Monday, January 9, 2023
- Last update times:
- Monday, February 6, 2023
- Last update rooms:
- Monday, February 6, 2023
- Type(s) / SWS (hours per week per semester)
- lecture (V) / 2
- Department
- Faculty of Technology
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