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This entry is from Winter semester 2018/19 and might be obsolete. You can find a current equivalent here.

CS 543 — Statistical Bioinformatics
(dt. Statistische Bioinformatik)

Level, degree of commitment Specialization module, compulsory elective module
Forms of teaching and learning,
workload		 Lecture (2 SWS), recitation class (2 SWS),
180 hours (60 h attendance, 120 h private study)
Credit points,
formal requirements		 6 CP
Course requirement(s): Oral or written examination
Examination type: Successful completion of at least 50 percent of the points from the weekly exercises as well as at least 2 presentations of the tasks.
Language,
Grading		 German,
The grading is done with 0 to 15 points according to the examination regulations for the degree program M.Sc. Computer Science.
Duration,
frequency		 One semester,
I.d.R. in jedem Sommersemester
Person in charge of the module's outline Prof. Dr. Dominik Heider

Contents

Selected topics from statistical bioinformatics that are required for calculations in the natural sciences, in particular from the field of biological data processing (e.g. alignments, encoding), as well as methods from the fields of statistics (e.g. statistical tests, evaluation) and static learning (e.g. Random Forests). The methods are presented in the lecture. During the exercise, their application will be practiced using concrete case studies. In the practical course a project is worked out independently with the help of the presented methods.


Qualification Goals

The students are familiar with the most important methods from statistical bioinformatics that are required for calculations in the natural sciences. They have understood these methods and are able to select, execute and implement suitable procedures for concrete case studies.


Prerequisites

Translation is missing. Here is the German original:

Keine. Empfohlen werden die Kompetenzen, die in den Modulen Einführung in die Bioinformatik sowie Grundlagen der Statistik vermittelt werden.


Applicability

Module imported from M.Sc. Computer Science.

It can be attended at FB12 in study program(s)

  • B.Sc. Computer Science
  • M.Sc. Data Science
  • M.Sc. Computer Science

When studying M.Sc. Data Science, this module can be attended in the study area Specialization Modules in Computer Science.


Recommended Reading

  • Witten, Frank und Hall: Data Mining, Morgan Kaufmann
  • Weitere Literatur wird in der Lehrveranstaltung bekanntgegeben.



Please note:

This page describes a module according to the latest valid module guide in Winter semester 2018/19. Most rules valid for a module are not covered by the examination regulations and can therefore be updated on a semesterly basis. The following versions are available in the online module guide:

The module guide contains all modules, independent of the current event offer. Please compare the current course catalogue in Marvin.

The information in this online module guide was created automatically. Legally binding is only the information in the examination regulations (Prüfungsordnung). If you notice any discrepancies or errors, we would be grateful for any advice.