Official course description:

Full info last published 15/11-21
Course info
ECTS points:
Course code:
Participants max:
Offered to guest students:
Offered to exchange students:
Offered as a single subject:
Price for EU/EEA citizens (Single Subject):
10625 DKK
MSc. Master
MSc in Computer Science
Course manager
Associate Professor
Course semester
Forår 2022
31 January 2022
31 August 2022
Exam type
ekstern censur
Grade Scale
Exam Language
This is a course in mathematics covering linear algebra and basic probability theory. This course is the first course of the Algorithms and Machine Learning specialisations.

The topics covered by this course are important in various branches of computer science, in particular in algorithms and machine learning. The topics covered in the linear algebra part of the course include systems of linear equations, matrices, determinants, vector spaces, bases, dimension, and eigenvectors. The topics covered in the probability theory part include conditional probability, Bayes theorem, discrete and continuous random variables, as well as the limit theorems. The course focuses on providing a basic understanding of the mathematical concepts covered, but will also include a few illustrative examples of their use, such as least squares analysis.

Formal prerequisites
The course assumes that the students have taken the course Foundation of Computing: Discrete Mathematics from the BSc in Software Development or similar.
Intended learning outcomes

After the course, the student should be able to:

  • Solve systems of linear equations
  • Define the basic concepts of linear algebra and probability, e.g., eigenvalues for a matrix or variance of a discrete random variable
  • Compute the essential constructions of linear algebra, such as the inverse of a given matrix or the eigenvectors of a given matrix. Compute probabilities, expected values, variances and other concepts from probability theory.
  • Apply the tools of linear algebra and probability to solve small mathematical problems.
  • Model simple probabilistic problems using the distributions covered in the course.
Learning activities

Lectures and exercises.
At the exercise sessions the students will solve and present solutions to mathematical problems. The mandatory assignments are of the form of mathematical problems, but can also involve some programming. The solutions must be submitted in written form.

Mandatory activities
There are 6 mandatory assignments, out of which 5 must be approved for the student to qualify for the exam. The deadlines are evenly distributed over the semester (approximately one every 2 weeks), exact dates will be posted on learnit the first week of the semester. If a mandatory assignment is not approved the first time, the student will be allowed to resubmit approximately one week after the first deadline. 

The student will receive the grade NA (not approved) at the ordinary exam, if the mandatory activities are not approved and the student will use an exam attempt.

Course literature

Ron Larson: Elementary Linear Algebra, 8th ed, metric version

Anderson, Seppäläinen and Valkó: Introduction to Probability, Cambridge University Press

Student Activity Budget
Estimated distribution of learning activities for the typical student
  • Preparation for lectures and exercises: 40%
  • Lectures: 20%
  • Exercises: 20%
  • Assignments: 20%
Ordinary exam
Exam type:
A: Written exam on premises, External (7-point scale)
Exam variation:
A33: Written exam on premises on paper with restrictions
Exam duration:
4 hours
Aids allowed for the exam:
Written and printed books and notes
E-books and/or other electronic devices
  • E-books on laptops and tablets.
    Notes on laptops and tablets

Time and date