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Data Mining I
Last Updated: 2026-02-05 15:35:52
Abstract
Data Mining, the search for statistical dependencies in large databases, is of utmost important in modern society, in particular in biological and medical research. This course provides an introduction to the key problems, concepts, and algorithms in data mining, and the applications of data mining in computational biology.
Objective
The goal of this course is that the participants gain an understanding of data mining problems and algorithms to solve these problems, in particular in biological and medical applications.
Content
The goal of the field of data mining is to find patterns and statistical dependencies in large databases, to gain an understanding of the underlying system from which the data were obtained. In computational biology, data mining contributes to the analysis of vast experimental data generated by high-throughput technologies, and thereby enables the generation of new hypotheses. In this course, we will present the algorithmic foundations of data mining and its applications in computational biology. The course will feature an introduction to popular data mining problems and algorithms, reaching from classification via clustering to feature selection. This course is intended for both students who are interested in applying data mining algorithms and students who would like to gain an understanding of the key algorithmic concepts in data mining. Tentative list of topics: 1. Distance functions 2. Classification 3. Clustering 4. Feature Selection
Resources
Lecture Notes
Course material will be provided in form of slides.
Literature
Will be provided during the course.
General Information
- Language
- English
- Levels
- MSC
- Frequency
- Yearly recurring
Examination
- Type
- session examination
- Mode
- written 90 minutes
- Aids
- None
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| lecture with exercise |
Data Mining I
Tutorial: 8-9h, Lecture: 9-11h.
ATTENTION: Course starts on Wednesday, September 30
The lecturers will communicate the exact lesson times of ONLINE courses.
|
|
3 h weekly |
| independent project |
Data Mining I
Project Work (compulsory continuous performance assessment), no fixed presence required.
|
No time listed | 2 h weekly |
Offered In
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Computational Biology and Bioinformatics Master (More informations at: )
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Core Courses (Please note that the list of core courses is a closed list. Other courses cannot be added to the core course category in the study plan. Also the assignments of courses to core subcategories cannot be changed. Students need to pass at least one course in each core subcategory. A total of 40 ECTS needs to be acquired in the core course category.)
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Advanced Courses (Students need to aquire a total of 24 ECTS in this category. The list of advanced courses is a closed list, no other course can be added to this category.)
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