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636-0018-00L 6 Credits DR , MSC D-BSSE , D-INFK

Data Mining I

Lecturers & Examiners: Dr. Karsten Michael Borgwardt
VVZ CR n/a

Last Updated: 2026-02-05 16:01:42

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
DR , MSC
Frequency
Yearly recurring

Examination

Type
end-of-semester examination
Mode
written 90 minutes
Aids
None
Final grade: 70% written examination, 30% project workProject work has to be re-done in case of repetitionThe course includes up to 6 compulsory continuous performance assessments in form of biweekly homework assignments, which constitute 30% of the final grade

Course Components

Type Title Time & Place Hours
lecture with exercise Data Mining I
Takes place at the D-BSSE in Basel and is transmitted per video conference to Zürich (HG D16.2) Tutorial: 8-9h, Lecture: 9-11h. ATTENTION: Course starts on Wednesday, September 28
  • Wed 08:15-11:00 (BSA E 46)
  • Wed 08:15-11:00 (HG D 16.2)
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