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This entry is from Winter semester 2016/17 and might be obsolete. You can find a current equivalent here.
CS 572 — Information Retrieval
(dt. Information Retrieval)
Level, degree of commitment | Specialization module, depends on importing study program |
Forms of teaching and learning, workload |
Lecture (2 SWS), recitation class (2 SWS), 180 hours (60 h attendance, 120 h private study) |
Credit points, formal requirements |
6 CP Course requirement(s): Oral or written examination Examination type: Successful completion of at least 50 percent of the points from the weekly exercises as well as at least 2 presentations of the tasks. |
Language, Grading |
German,The grading is done with 0 to 15 points according to the examination regulations for the degree program M.Sc. Data Science. |
Origin | M.Sc. Data Science |
Duration, frequency |
One semester, Zweijährlich im Sommersemester |
Person in charge of the module's outline | Prof. Dr. Bernhard Seeger |
Contents
- Quality criteria for information retrieval
- Models for Information Retrieval
- Architecture of Information Retrieval Systems
- Index Methods and Index Structure
- query extension
- IR and Web
- multimedia retrieval
Qualification Goals
Students should
- Gain knowledge of the most important models for information retrieval,
- get an overview of the architecture of IR systems,
- Know indexing techniques,
- Understand optimization of queries in IR,
- acquire knowledge in applications of IR in the field of web and multimedia,
- practice scientific working methods (recognizing, formulating, solving problems, training the ability of abstraction),
- practice oral communication skills in the exercises by practicing free speech in front of an audience and in discussion.
Prerequisites
Translation is missing. Here is the German original:
Keine. Empfohlen werden die Kompetenzen, die in dem Modul Algorithmen und Datenstrukturen vermittelt werden
Applicability
The module can be attended at FB12 in study program(s)
- B.Sc. Data Science
- B.Sc. Computer Science
- B.Sc. Business Informatics
- M.Sc. Data Science
- M.Sc. Computer Science
- M.Sc. Mathematics
- M.Sc. Business Informatics
- LAaG Computer Science
When studying M.Sc. Data Science, this module can be attended in the study area Specialization Modules in Computer Science.
The module can also be used in other study programs (export module).
Recommended Reading
- Manning, Raghavan, Schütze: Introduction to Information Retrieval, Cambridge University Press
- Baeza-Yates, Ribeiro-Neto: Modern Information Retrieval, Addison Wesley
- Ferber: Information Retrieval-Suchmodelle und Data-Mining-Verfahren für Textsammlungen und das Web, dpunkt Verlag
- Henrich: Information Retrieval - Grundlagen, Modelle und Anwendungen
Please note:
This page describes a module according to the latest valid module guide in Winter semester 2016/17. Most rules valid for a module are not covered by the examination regulations and can therefore be updated on a semesterly basis. The following versions are available in the online module guide:
- Winter 2016/17
- Summer 2018
- Winter 2018/19
- Winter 2019/20
- Winter 2020/21
- Summer 2021
- Winter 2021/22
- Winter 2022/23
- Winter 2023/24
The module guide contains all modules, independent of the current event offer. Please compare the current course catalogue in Marvin.
The information in this online module guide was created automatically. Legally binding is only the information in the examination regulations (Prüfungsordnung). If you notice any discrepancies or errors, we would be grateful for any advice.