636-0018-00L Data Mining I
Semester | Autumn Semester 2017 |
Lecturers | K. M. Borgwardt |
Periodicity | yearly recurring course |
Language of instruction | English |
Courses
Number | Title | Hours | Lecturers | |||||||
---|---|---|---|---|---|---|---|---|---|---|
636-0018-00 G | Data Mining I 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 8-11h (BSA E46 / HG D16.2) Attention: Lecture starts on Wednesday, October 4 (No classes on 20.09. and 27.09). | 3 hrs |
| K. M. Borgwardt | ||||||
636-0018-00 A | Data Mining I Project Work, no fixed presence required. | 2 hrs | K. M. Borgwardt |
Catalogue data
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 |
Lecture notes | Course material will be provided in form of slides. |
Literature | Will be provided during the course. |
Prerequisites / Notice | Basic 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 credits | 6 credits |
Examiners | K. M. Borgwardt |
Type | session examination |
Language of examination | English |
Repetition | The performance assessment is only offered in the session after the course unit. Repetition only possible after re-enrolling. |
Mode of examination | written 90 minutes |
Additional information on mode of examination | Final grade: 70% written examination, 30% project work Project work has to be re-done in case of repetition |
Written aids | None |
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
Programme | Section | Type | |
---|---|---|---|
Biotechnology Master | Biomelecular-Orientated | W | |
Biotechnology Master | System-Orientated | W | |
Computational Biology and Bioinformatics Master | Data Science | W |