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636-0019-00L 6 Credits MSC D-BSSE , D-INFK
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Data Mining II

Lecturers & Examiners: Dr. Karsten Michael Borgwardt
Prerequisites: Basic understanding of mathematics, as taught in basic mathematics courses at the Bachelor`s level. Ideally, students will have attended Data Mining I before taking this class.
VVZ CR n/a

Last Updated: 2026-02-05 15:55:01

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
MSC
Frequency
Yearly recurring

Examination

Type
session 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 project work 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 II
The lecture will be held ONLINE only until the end of the semester. ATTENTION: Lecture starts Wednesday, March 3 (no lecture and tutorial in first week) Lecture: Wednesday 14-16h Tutorial: 16-17h The lecturers will communicate the exact lesson times of ONLINE courses.
  • Wed 14:00-17:00 (ON LI NE)
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

  • Biotechnology Master
    • Electives (Electives may be taken at D-BSSE or at Uni Basel. The mentor may assign other courses to the electives category based on student`s formal request. Courses offered in the advanced courses category may also be taken as electives.)
  • Computational Biology and Bioinformatics Master (More informations at: )
    • 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.)