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This entry is from Winter semester 2021/22 and might be obsolete. You can find a current equivalent here.
CS 572 — Information Retrieval
(dt. Information Retrieval)
Level, degree of commitment | Specialization module, compulsory elective module |
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): Successful completion of at least 50 percent of the points from the weekly exercises as well as at least 2 presentations of the tasks. Examination type: Oral or written examination |
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. |
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
The students shall
- Acquire knowledge of the most important models for information retrieval,
- get an overview of the architectures of IR systems,
- know fundamental indexing techniques,
- Understand optimization of requests in IR,
- Acquire knowledge of IR applications in web and multimedia,
- practice scientific working methods (recognizing, formulating, solving problems, training the ability to abstract),
- practice oral communication skills in the exercises by practicing free speech in front of an audience and during discussion.
Prerequisites
None. The competences taught in the following modules are recommended: either Algorithms and Data Structures or Practical Informatics II: Data Structures and Algorithms for Pre-Service-Teachers.
Applicability
Module imported from M.Sc. Data Science.
It 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
- M.Sc. Business Mathematics
- LAaG Computer Science
When studying M.Sc. Mathematics, this module can be attended in the study area Minor subject Computer Science.
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 2021/22. 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.