636-0018-00L  Data Mining I

SemesterAutumn Semester 2019
LecturersK. M. Borgwardt
Periodicityyearly recurring course
Language of instructionEnglish



Courses

NumberTitleHoursLecturers
636-0018-00 GData Mining I
Tutorial: 8-9h, Lecture: 9-11h. The tutorial and lecture will be held each Wednesday in Basel and will be transmitted via videoconference to Zurich.
ATTENTION: Course starts on Wednesday, October 2!
Course will be streamed and recorded
3 hrs
Wed08:15-11:00BSA E 46 »
08:15-11:00HG D 16.2 »
K. M. Borgwardt
636-0018-00 AData Mining I
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. This course provides an introduction to the key problems, concepts, and algorithms in data mining, and the applications of data mining in computational biology.
ObjectiveThe 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.
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 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
Lecture notesCourse material will be provided in form of slides.
LiteratureWill be provided during the course.
Prerequisites / NoticeBasic understanding of mathematics, as taught in basic mathematics courses at the Bachelor's level.

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.

Groups

No information on groups available.

Restrictions

There are no additional restrictions for the registration.

Offered in

ProgrammeSectionType
Biotechnology MasterBiomelecular-OrientatedWInformation
Biotechnology MasterSystem-OrientatedWInformation
Computational Biology and Bioinformatics MasterData ScienceWInformation