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This entry is from Winter semester 2022/23 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, workload |
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): 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 examination |
Language, Grading |
German,The grading is done with 0 to 15 points according to the examination regulations for the degree program B.Sc. Computer Science. |
Duration, frequency |
One semester, each winter semester |
Person in charge of the module's outline | Prof. Dr. Alfred Ultsch |
Contents
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.
Prerequisites
None. The competences taught in the following modules are recommended: Object-oriented Programming, either Algorithms and Data Structures or Practical Informatics II: Data Structures and Algorithms for Pre-Service-Teachers, System Software and Computer Communication.
Applicability
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
- M.Sc. Business Mathematics
- LAaG Computer Science
When studying M.Sc. Data Science, this module can be attended in the study area Specialization Modules in 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 2022/23. 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 (no corresponding element)
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.