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This entry is from Winter semester 2016/17 and might be obsolete. No current equivalent could be found.

Mathematical Data Analysis
(dt. Mathematische Datenanalyse)

Level, degree of commitment Advanced module, compulsory elective module
Forms of teaching and learning,
workload		 Lecture (4 SWS), recitation class (2 SWS),
270 hours (90 h attendance, 180 h private study)
Credit points,
formal requirements		 9 CP
Course requirement(s): Written or oral examination
Examination type: Successful completion of at least 50 percent of the points from the weekly exercises.
Language,
Grading		 German,
The grading is done with 0 to 15 points according to the examination regulations for the degree program B.Sc. Data Science.
Duration,
frequency		 One semester,
Regularly alternating with other specialization modules
Person in charge of the module's outline Prof. Dr. István Heckenberger

Contents

  • Presentation of problems for the evaluation of large amounts of data,
  • Modelling of the tasks in the form of matrix equations,
  • Mathematical and algorithmic methods for matrix factorization,
  • Special algorithms under positivity and for thin matrix fillings,
  • tensor factorization

Qualification Goals

The students shall

  • Get to know methods for investigating large amounts of data,
  • understand the mathematical background of the applied algorithms,
  • learn to combine techniques from mathematics and computer science,
  • Improve their oral communication skills in the tutorials by practicing free speech in front of an audience and during discussion.

Prerequisites

Translation is missing. Here is the German original:

Keine. Empfohlen werden die Kompetenzen, die in den Basismodulen Grundlagen der linearen Algebra und Objektorientierte Programmierung vermittelt werden.


Applicability

Module imported from B.Sc. Data Science.

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

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

When studying B.Sc. Computer Science, this module can be attended in the study area Minor subject Mathematics.


Recommended Reading

  • Cichocki u.a.: Nonnegative Matrix and Tensor Factorizations



Please note:

This page describes a module according to the latest valid module guide in Winter semester 2016/17. 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.