Data and Knowledge Engineering
Sommer
(in German: Data and Knowledge Engineering )
Module-ID: FIN-INF-120500 |
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Abbreviation
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Credit Points 6 |
Semester Sommer |
Term starting at 1. |
Duration 1 Semester |
Language english |
Level Master |
| Link: | LSF |
| Limited accessability: | |
| Responsibility: | Andreas Nürnberger |
| Lecturer: | Andreas Nürnberger |
| Classes: | Seminar Data and Knowledge Engineering |
| Applicability in curriculum: | - M.Sc. INF: Schlüssel- und Methodenkompetenzen - M.Sc. INGINF: Schlüssel- und Methodenkompetenzen - M.Sc. WIF: Schlüssel- und Methodenkompetenzen - M.Sc. DKE: Fundamentals of Data Science - M.Sc. VC: Schlüssel- und Methodenkompetenzen |
Intended learning outcomes:
Students who complete the course ...
- understand how to work in practice with DKE approaches
- can perform a literature review on the state-of-the-art on a given topic
- can describe a given DKE task in form of a scientific paper
- can present and defend a practical project by a presentation and discussion
Content:
The seminar focuses on applying machine learning techniques to a hands-on problem, resulting in a paper and short presentation in one of the following research areas:
Information Retrieval:
- Document organization
- Information management
- Ranking & Explainability
- Named Entity Recognition
- Tokenization & Filtering
- Latent Representations of Words / Sentences / Documents
- Usability
- User Experience (UX) Design
- Human-AI interaction
Workload:
180 h self study
| Pre-examination requirements: | Type of examination: | Teaching method / lecture hours per week (SWS): |
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Written report and oral presentation (Referat). Requirements for exam participation will be announced in the first week of the course in class and online. |
Seminar |
| Prerequisites according to examination regulations: | Recommended prerequisites: |
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keine |
| Media: | Literature: |
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Comments: