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Data Mining II
Last Updated: 2026-02-05 16:07:57
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. Building on the basic algorithms and concepts of data mining presented in the course "Data Mining I", this course presents advanced algorithms and concepts from data mining and the state-of-the-art in applications of data mining.
Objective
The goal of this course is that the participants gain an advanced understanding of data mining problems and algorithms to solve these problems, in particular in biological and medical applications, and to enable them to conduct their own research projects in the domain of data mining.
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 advanced topics in data mining and its applications in computational biology. Tentative list of topics: 1. Dimensionality Reduction 2. Association Rule Mining 3. Text Mining 4. Graph Mining
Resources
Lecture Notes
Course material will be provided in form of slides.
Literature
Will be provided during the course.
General Information
- Language
- English
- Levels
- DR , 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 II
Lecture: Wednesday 14-16h
Tutorial: 16-17h
This lecture will take place online only (via Zoom). Link will be send to registered students in due time.
Room reserved in Basel: Oppenheim
Room reserved in ZH: HG D 16.2
|
|
3 h weekly |
| independent project |
Data Mining II
Project Work (compulsory continuous performance assessment), no fixed presence required.
|
No time listed | 2 h weekly |
Offered In
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Electives (The electives list in the ETH course catalogue is an open list, and the courses listed in the ETH course catalogue provide just examples for possible elective courses, e.g. a selection of eligible courses. Students are expected to look for relevant courses in the ETH and University of Basel course catalogue and ask their mentor for approval. Courses from the advanced course category may also be taken as electives. We particularly recommend browsing the University of Basel course catalogue for elective courses of relevant master's degree programes (using the filter "programe structure" on the course catalogue website), such as for example: Biomedical Engineering, Chemistry, Drug Sciences, Epidemiology, Infection Biology, Molecular Biology, Nanosciences.)
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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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Doctorate Biosystems Science and Engineering (More Information at: )
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Subject Specialisation (The courses on offer below are a selection out of a much larger available number of courses. You may look for other courses too. If you are uncertain about the creditability and assessment of the course unit you wish to take, please consult the D-​BSSE Doctoral Administration. This should be done before registering the course unit.)
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