Data Mining and Knowledge Discovery
This module handbook serves to describe contents, learning outcome, methods and examination type as well as linking to current dates for courses and module examination in the respective sections.
IN2030 is a semester module in English language at Master’s level which is offered in winter semester.
This Module is included in the following catalogues within the study programs in physics.
- Catalogue of non-physics elective courses
|Total workload||Contact hours||Credits (ECTS)|
|90 h||30 h||3 CP|
Content, Learning Outcome and Preconditions
- data preprocessing and filtering
- data visualization
- data projections (principal component analysis, multidimensional scaling, Sammon mapping, auto associator)
- data transformation and feature selection
- correlation and regression
- classification (Bayes, discriminance, support vector machine, nearest neighbor, learning vector quantization, decision trees)
- clustering (sequential, protype based, fuzzy, relational, heuristic)
- understand the different types of data and relations;
- understand, apply, and evaluate data preparation, analysis, and visualization methods;
- understand, apply, and evaluate linear and nonlinear correlation, regression and forecasting methods;
- are able to compare classification and clustering, and to understand, apply, and evaluate the corresponding methods;
- are able to select, apply, and evaluate suitable data mining methods for given applications.
The main didactic goal is to introduce students to a variety of methods and provide them with the basic notions necessary to extend their knowledge by accessing the literature on their own. The work that the students must invest to achieve this goal corresponds the 3 credits assigned to the module.
Courses, Learning and Teaching Methods and Literature
Courses and Schedule
|VO||2||Data Mining and Knowledge Discovery (IN2030)||Runkler, T.||
Mon, 08:30–10:00, virtuell
Learning and Teaching Methods
- Tan, Steinbach, Kumar: Introduction to Data Mining. Addison Wesley
- Dunham: Data Mining - Introductory and Advanced Topics. Prentice Hall.
- Theodoridis, Koutroumbas: Pattern Recognition. Academic Press
Description of exams and course work
The exam may be repeated at the end of the semester.