Data Mining and Knowledge Discovery
Module IN2030
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.
Basic Information
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
Content
- data sources, characteristics, and errors
- 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
- forecasting
- classification (Bayes, discriminance, support vector machine, nearest neighbor, learning vector quantization, decision trees)
- clustering (sequential, protype based, fuzzy, relational, heuristic)
- 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
- forecasting
- classification (Bayes, discriminance, support vector machine, nearest neighbor, learning vector quantization, decision trees)
- clustering (sequential, protype based, fuzzy, relational, heuristic)
Learning Outcome
On successful completion of the module, students
- 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.
- 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.
Preconditions
Basic mathematics
Courses, Learning and Teaching Methods and Literature
Courses and Schedule
WS 2022/3
WS 2021/2
WS 2020/1
WS 2019/20
WS 2018/9
WS 2017/8
WS 2016/7
WS 2015/6
WS 2014/5
WS 2013/4
WS 2012/3
| Type | SWS | Title | Lecturer(s) | Dates | Links |
|---|---|---|---|---|---|
| VO | 2 | Data Mining and Knowledge Discovery (IN2030) | Runkler, T. |
Mon, 08:30–10:00, virtuell |
eLearning |
Learning and Teaching Methods
The module consists of a lecture. The lecture content is communicated via lectures and presentations. The students shall be motivated to study the literature, to work on exercise problems, and to become familiar with the content.
Media
Lecture notes, slides, board
Literature
- Runkler: Data Analytics, Springer
- Tan, Steinbach, Kumar: Introduction to Data Mining. Addison Wesley
- Dunham: Data Mining - Introductory and Advanced Topics. Prentice Hall.
- Theodoridis, Koutroumbas: Pattern Recognition. Academic Press
- Tan, Steinbach, Kumar: Introduction to Data Mining. Addison Wesley
- Dunham: Data Mining - Introductory and Advanced Topics. Prentice Hall.
- Theodoridis, Koutroumbas: Pattern Recognition. Academic Press
Module Exam
Description of exams and course work
The exam takes the form of a written 60 minutes test. Questions allow to assess the acquaintance with the different types of data, relations and algorithms of data mining, and to assess the ability to select, apply, and evaluate suitable data mining methods (correlation, regression, forecasting, classification, clustering).
Exam Repetition
The exam may be repeated at the end of the semester.