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This entry is from Winter semester 2016/17 and might be obsolete. No current equivalent could be found.

CS 591 — Knowledge Discovery
(dt. Knowledge Discovery)

Level, degree of commitment Advanced module, compulsory elective module
Forms of teaching and learning,
Lecture (4 SWS), recitation class (2 SWS),
270 hours (90 h attendance, 180 h private study)
Credit points,
formal requirements
9 CP
Course requirement(s): Oral 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.
The grading is done with 0 to 15 points according to the examination regulations for the degree program B.Sc. Computer Science.
One semester,
each winter semester
Person in charge of the module's outline Prof. Dr. Alfred Ultsch


To discover new, useful and for human experts understandable knowledge in data collections is a frequent task in research and application. It requires knowledge in statistics but also in methods of artificial intelligence (machine learning, expert systems, knowledge acquisition and processing). In particular, data-bionic methods borrowed from nature, such as neural networks, swarm systems and emergent self-organizing systems. The knowledge gained should be understandable for people as well as algorithmically usable in expert systems. The lecture conveys the knowledge from the mentioned fields necessary for such a knowledge discovery from databases.

Qualification Goals

The students shall

  • learn scientific procedures for the investigation of data collections with the aim of discovering new and hitherto unknown knowledge,
  • explorative statistical methods for the description and analysis of data, methods of visualization and projection of high-dimensional, different methods for clustering data and their peculiarities, methods of machine learning for the construction of classifiers, types of knowledge and expert systems,
  • acquire knowledge of natural analog methods of knowledge discovery (neural networks, swarm systems, emergent self-organization),
  • 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.


Translation is missing. Here is the German original:

Keine. Empfohlen werden die Kompetenzen, die in den Basismodulen der Informatik vermittelt werden.


Module imported from B.Sc. Computer 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
  • LAaG Computer Science

When studying M.Sc. Mathematics, this module can be attended in the study area Minor subject Computer Science.

Recommended Reading

  • D. Hand, H. Mannila, P. Smyth: Principles of Data Mining. MIT Press, 2001; T. Hastie , R. Tibshirani , J. H. Friedman: The Elements of Statistical Learning, Springer, 2001; R. O. Duda, P. E. Hart, D.G. Stork: Pattern Classification, John Wiley, 2001

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:

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.