(dt. Knowledge Discovery)
|Level, degree of commitment in original study programme||Intermediate 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)
Course requirement: 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
|German,The grading is done with 0 to 15 points according to the examination regulations for study course B.Sc. Computer Sciences.|
|Original study programme||B.Sc. Informatik / Informatik Wahlpflichtmodule|
|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.
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.
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.
- 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
This page describes a module according to the latest valid module guide in Wintersemester 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:
- WiSe 2016/17 (no corresponding element)
- SoSe 2018 (no corresponding element)
- WiSe 2018/19
- WiSe 2019/20
- WiSe 2020/21
- SoSe 2021
- WiSe 2021/22
- WiSe 2022/23
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.