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CS 608 — Matrix Methods in Data Analysis
(dt. Matrixmethoden in der 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): Successful completion of at least 50 percent of the points from the weekly exercises.
Examination type: Written or oral examination (individual examination)
Language,
Grading		 German/English,
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

Students will be able to,

  • Understand methods for studying large data sets and the mathematical background of the algorithms used,
  • combine techniques from mathematics and computer science,
  • speak freely about scientific content, both in front of an audience and in a discussion.

Prerequisites

None. The competences taught in the following modules are recommended: either Linear Algebra I and Linear Algebra II or Basic Linear Algebra, Object-oriented Programming.


Applicability

Module imported from B.Sc. Data Science.

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

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

When studying M.Sc. Data Science, this module can be attended in the study area Compulsory Elective Modules in Mathematics.


Recommended Reading

  • Skillicorn, D., Understanding Complex Datasets. Data Mining with Matrix Decompositions, Chapman & Hall/CRC, 2007
  • Elden, L., Matrix Methods in Data Mining and Pattern Recognition, SIAM, Second edition, 2019
  • Strang, G., Linear Algebra and Learning from Data, Welleslay-Cambridge Press, 2019



Please note:

This page describes a module according to the latest valid module guide in Winter semester 2023/24. 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:

  • Winter 2016/17 (no corresponding element)
  • Summer 2018 (no corresponding element)
  • Winter 2018/19 (no corresponding element)
  • Winter 2019/20 (no corresponding element)
  • Winter 2020/21 (no corresponding element)
  • Summer 2021 (no corresponding element)
  • Winter 2021/22 (no corresponding element)
  • Winter 2022/23 (no corresponding element)
  • Winter 2023/24

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.