Introduction lecture: https://uni-bielefeld.sciebo.de/s/ASim0O9RiFwnfIK
The course will cover three main topics: Diffusion Models [1], Transformers [2], and Large Language Models. This will include DALL-E, 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.
Lectures will begin on Monday, April 3 at 8:30 am ONLINE [3] with an introductory lecture explaining the topics and structure of the course. April 10 - holiday. April 17 - 2nd lecture.
Tutorials will begin on Tuesday, April 25 at 8:30 am ONLINE [3].
Most of the lectures will be available in video recordings.
Communication via Discord server [4], no E-mails.
[1] https://github.com/huggingface/diffusers
[2] https://github.com/huggingface/transformers
[3] Zoom: https://uni-bielefeld.zoom.us/j/67461280386?pwd=NUNRSjZHV1FicGlack1JWmJHTDFVZz09 Meeting ID: 674 6128 0386 Passcode: 123123
[4] https://discord.gg/ZH9CBGF6
Rhythmus | Tag | Uhrzeit | Format / Ort | Zeitraum |
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Modul | Veranstaltung | Leistungen | |
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39-M-Inf-VDM Vertiefung Datamining | Datamining II | Studieninformation |
Die verbindlichen Modulbeschreibungen enthalten weitere Informationen, auch zu den "Leistungen" und ihren Anforderungen. Sind mehrere "Leistungsformen" möglich, entscheiden die jeweiligen Lehrenden darüber.
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.