392128 Tutorial Deep Learning for Data Mining (Ü) (SoSe 2024)

Contents, comment

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.

Lectures will begin on Monday, April 8 at 8:30 am ONLINE [1] with an introductory lecture explaining the topics and structure of the course.
Tutorials will begin on Tuesday, April 30 at 8:30 am ONLINE [3].
Most of the lectures will be available in video recordings.

[1] Zoom: https://uni-bielefeld.zoom.us/j/67461280386?pwd=NUNRSjZHV1FicGlack1JWmJHTDFVZz09
Meeting ID: 674 6128 0386
Passcode: 123123

Discord channel lecture-2024sose: https://discord.gg/qhtARBhN

Teaching staff

Dates ( Calendar view )

Frequency Weekday Time Format / Place Period  
weekly Di 8-10 U2-107 08.04.-19.07.2024

Subject assignments

Module Course Requirements  
39-M-Inf-AI-app Applied Artificial Intelligence Applied Artificial Intelligence: Übung Student information
39-M-Inf-AI-app-foc Applied Artificial Intelligence (focus) Applied Artificial Intelligence (focus): Übung Student information
39-M-Inf-VDM Vertiefung Datamining Datamining II 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.

No E-Learning Space
Registered number: 36
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_392128@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_450974060@ekvv.uni-bielefeld.de
Coverage:
36 Students to be reached directly via email
Notes:
Additional notes on the electronic mailing lists
Last update basic details/teaching staff:
Wednesday, January 10, 2024 
Last update times:
Thursday, February 1, 2024 
Last update rooms:
Thursday, February 1, 2024 
Type(s) / SWS (hours per week per semester)
Ü / 2
Language
This lecture is taught in english
Department
Faculty of Technology
Questions or corrections?
Questions or correction requests for this course?
Planning support
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=450974060
Send page to mobile
Click to open QR code
Scan QR code: Enlarge QR code
ID
450974060