DM561: Linear algebra with applications

Study Board of Science

Teaching language: Danish or English depending on the teacher, but English if international students are enrolled
EKA: N330024102
Assessment: Second examiner: External
Grading: 7-point grading scale
Offered in: Odense
Offered in: Autumn
Level: Bachelor

STADS ID (UVA): N330024101
ECTS value: 10

Date of Approval: 25-04-2019

Duration: 1 semester

Version: Archive


New courser E2018 (Autumn 2018)
The course is co-read with:The first part of the course is co-read with MM505.                        

Entry requirements

The course cannot be chosen by students, who have passed MM505 or the first part of MM538. 

Academic preconditions

Students taking the course are expected to: The content of DM550 Introduction to Programming should be known.
The course cannot be chosen by students, who have passed MM505 or the first part of MM538. 

Course introduction

The main aim of the course is to introduce the student to the basic concepts and methods of linear algebra, with emphasis on matrix manipulations. The material covered is important in many aspects of computer science and has widespread applications throughout the sciences, and another aim of the course is to teach the students how to apply the methods of the course in such practical settings via programming. 

This course provides the students with the necessary prerequisites for several courses, in particular DM5XX (Data Mining and Machine Learning), that appears later in the degree.

In relation to the competence profile of the
degree it is the explicit focus of the course to:

  • Give skills in
    describing, formulating and disseminating problems and results to either other
    professional or non-specialists or collaborative partners and users
  • Give skills to apply
    thinking and terminology from the subject’s basic disciplines.
  • Give skills to present
    mathematical and computational thinking both in written and oral form.
  • Give skills to solve
    systems of linear equations, calculate determinants, find inverses of matrices,
    find coordinates of vectors, find matrices of linear transformations.
  • Give knowledge and
    understanding of vector spaces and linear transformations.
  • Give the competence to
    handle complex and development-oriented situations in study and work contexts
  • Give the competence to
    identify one's own needs for learning and structure one's own learning in
    different learning environments

Expected learning outcome

The learning objectives of the course are that the student demonstrates the ability to:
  • Reproduce definitions and results covered in the course.
  • Be able to use these results to analyse concrete examples.
  • Formulate and present definitions and calculations in a mathematically rigorous way.
  • Make programs which uses the methods from the course.


The following main topics are contained in the course:
  • Systems of linear equations
  • Matrix operations, inverses, determinants
  • Vector spaces, basis, coordinates, linear independence
  • Linear transformations, eigenvalue problems, diagonalisation
  • Linear algebra and programming
  • Applications of linear algebra in computer science

Applications of linear algebra, such as:

  • image processing
  • pagerank
  • least squares
  • graph algorithms 
  • methods for biology and chemistry
  • and others.


See Blackboard for syllabus lists and additional literature references.

Examination regulations

Exam element a)




Mandatory assignments




Second examiner: External


7-point grading scale


Full name and SDU username


Normally, the same as teaching language

Examination aids

To be announced during the course

ECTS value


Additional information

The examination form for re-examination may be different from the exam form at the regular exam.

Indicative number of lessons

88 hours per semester

Teaching Method

The intro phase facilitates an introduction to new material and topics, which in the skills training phase are processed with exercises prepared at home and discussed in class to validate the acquired knowledge.

Study phase activities:
  • Reading from text books
  • Solving homeworks
  • Applying acquired knowledge to practical projects

Teacher responsible

Name E-mail Department
Marco Chiarandini

Additional teachers

Name E-mail Department City
Daniel Merkle
Wojciech Szymanski


Administrative Unit

Institut for Matematik og Datalogi (datalogi)

Team at Educational Law & Registration


Offered in


Recommended course of study

Profile Education Semester Offer period