636-0019-00L  Data Mining II

SemesterSpring Semester 2019
LecturersK. M. Borgwardt
Periodicityyearly recurring course
Language of instructionEnglish
CommentPrerequisites: 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.


636-0019-00 GData Mining II
The lecture will be held each Wednesday either in Zurich or Basel and will be transmitted via videoconference to the second location.
Lecture in Basel/Zürich: Wednesday 14-16h, Tutorial 16-17h (BSB E4 Room "Manser" / HG D16.2)
3 hrs
Wed14:15-17:00BSB E 4 »
14:15-17:00HG D 16.2 »
K. M. Borgwardt
636-0019-00 AData Mining II
Project Work (compulsory continuous performance assessment), no fixed presence required.
2 hrsK. M. Borgwardt

Catalogue data

AbstractData 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.
ObjectiveThe 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.
ContentThe 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
Lecture notesCourse material will be provided in form of slides.
LiteratureWill be provided during the course.

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits6 credits
ExaminersK. M. Borgwardt
Typesession examination
Language of examinationEnglish
RepetitionThe performance assessment is only offered in the session after the course unit. Repetition only possible after re-enrolling.
Mode of examinationwritten 90 minutes
Additional information on mode of examinationFinal grade: 70% written examination, 30% project work
Project work has to be re-done in case of repetition
The course includes up to 6 compulsory continuous performance assessments in form of biweekly homework assignments, which constitute 30% of the final grade
Written aidsNone
This information can be updated until the beginning of the semester; information on the examination timetable is binding.

Learning materials

No public learning materials available.
Only public learning materials are listed.


No information on groups available.


There are no additional restrictions for the registration.

Offered in

Biotechnology MasterElectivesWInformation
Computational Biology and Bioinformatics MasterData ScienceWInformation