Information Retrieval
(in German: Information Retrieval )
Module-ID: FIN-INF-110307 |
| Link: | LSF |
| Responsibility: | Andreas Nürnberger |
| Lecturer: | Andreas Nürnberger |
| Classes: |
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| Applicability in curriculum: | - M.Sc. INF: Informatik - M.Sc. INGINF: Informatik - M.Sc. WIF: Informatik - M.Sc. DKE: Applied Data Science - M.Sc. DE: Methoden der Informatik - M.Sc. VC: Computer Science |
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Abbreviation IR |
Credit Points 6 |
Semester Winter |
Term ab 3. |
Duration 1 Semester |
Language english |
Level Master |
Intended learning outcomes:
Students can:
- Design and implement advanced index structures for heterogeneous data modalities (textual, images) leveraging platforms such as Apache Solr, Apache Lucene
- Execute comprehensive text data pipelines, encompassing tokenization, normalization, stemming/lemmatization, and semantic enrichment for downstream retrieval tasks.
- Integrate index construction with sophisticated pre-processing techniques and develop interactive, user-centric search interfaces.
- Critically assess and benchmark retrieval system effectiveness using standard IR evaluation metrics (e.g., MAP, NDCG, Precision@k)
- Translate foundational Information Retrieval (IR) theories into practice by architecting and deploying a functional search engine prototype.
- Learning and implementing a functional search engine prototype
Content:
- Statistical properties of texts
- Retrieval models and data structures
- Relevance feedback
- Evaluation
- Structuring datasets (clustering, categorisation)
- Structure and algorithms for web search
- Interface design
Workload:
56h contact time + 124h self study
| Pre-examination requirements: | Type of examination: | Teaching method / lecture hours per week (SWS): |
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Written exam. Requirements for exam participation will be announced in the first week of the course in class and online. |
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| Prerequisites according to examination regulations: | Recommended prerequisites: |
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none |
| Media: | Literature: |
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