392197 Deep Learning for Natural Language Processing (V) (SoSe 2024)
Contents, comment
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This seminars involves the creation of effective systems for the processing of text by means of statistical methods. There will be significant challenge in integrating these components into a scalable system that can handle the large amount of data required to obtain high accuracy. In addition to the technical challenges in this course, the students will be required to read recent research papers related to the task and integrate these results into their solutions.
Teaching staff
Dates ( Calendar view )
| Frequency | Weekday | Time | Format / Place | Period | |
|---|---|---|---|---|---|
| weekly | Mi | 08-10 | X-E0-001 | 08.04.-19.07.2024
not on: 5/1/24 / 6/26/24 |
|
| one-time | Mi | 08-10 | T2-205 | 26.06.2024 |
Subject assignments
| Module | Course | Requirements | |
|---|---|---|---|
| 39-Inf-SNLP Statistical Natural Language Processing | Introduction to Statistical Natural Language Processing | Student information | |
| - | Ungraded examination Graded examination | Student information | |
| 39-Inf-WP-DS Data Science (Basis) | Einführende Vorlesung | Student information | |
| - | Graded examination | Student information | |
| 39-Inf-WP-KI Künstliche Intelligenz (Basis) | Einführende Vorlesung | Student information | |
| - | Graded examination | Student information | |
| 39-M-Inf-AI-adv-foc Advanced Artificial Intelligence (focus) | Advanced Artificial Intelligence (focus): Vorlesung | Graded examination
|
Student information |
| 39-M-Inf-AI-bas Basics of Artificial Intelligence | Basics of Artificial Intelligence: Vorlesung | Student information | |
| - | Ungraded examination | Student information | |
| 39-M-Inf-TMKD Text Mining and Knowledge Discovery | Text Mining and Knowledge Discovery | 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.
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: 61
- 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_392197@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_450172532@ekvv.uni-bielefeld.de
- Coverage:
- 61 Students to be reached directly via email
- Notes:
- Additional notes on the electronic mailing lists
- Email archive
- Number of entries 3
- Open email archive
- Last update basic details/teaching staff:
- Thursday, January 4, 2024
- Last update times:
- Wednesday, March 13, 2024
- Last update rooms:
- Wednesday, March 13, 2024
- Type(s) / SWS (hours per week per semester)
- V / 2
- Language
- This lecture is taught in english
- Department
- Faculty of Technology
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- https://ekvv.uni-bielefeld.de/kvv_publ/publ/vd?id=450172532
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