Main content

This entry is from Winter semester 2020/21 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): 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.
Subject, Origin Computer Science, 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.


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 2020/21. 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:

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.