Official course description:

Full info last published 31/07-19
Course info
ECTS points:
Course code:
Offered to guest students:
Offered to exchange students:
Offered as a single subject:
MSc. Master
MSc in Computer Science
Course manager
Associate Professor
Part-time Lecturer
Course semester
EfterÄr 2019
26 August 2019
31 January 2020

This is a complete 15 ECTS course on Machine Learning. Building on the math knowledge acquired from the course Linear Algebra and Probability, students will be introduced to Machine Learning during the first part of the course. In the second part, recent machine learning research will be addressed.


The course enables students to analyse machine learning algorithms, implement abstractly specified machine learning methods in an imperative programming language, modify machine learning methods to analyse practical datasets and convey the results.

Subjects include supervised learning; Bayesian decision theory; parametric, semi-parametric, and nonparametric methods; multivariate analysis; deep learning; reinforcement learning; kernel machines; clustering; graphical models; Bayesian estimation; ensemble methods, and statistical testing.

Formal prerequisites

Linear Algebra and Probability

Intended learning outcomes

After the course, the student should be able to:

  • Discuss, clearly explain, and reflect upon central machine learning concepts and algorithms.
  • Choose among and make use of the most important machine learning approaches in order to apply (match) them to practical problems.
  • Implement abstractly specified machine learning methods in an imperative programming language.
  • Combine and modify machine learning methods to analyse practical dataset and covey the results.
Learning activities

The first 10 weeks will consist of lectures and exercise sessions. The last 4 weeks will consist of project work where students will implement machine learning methods from a recent research article.

Mandatory activities

Active participation in exercise sessions (details will be given though LearnIT) form the mandatory activities. If the mandatory active participation is not approved, an alternative test will be held at the end of the course (see details on LearnIT). 

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

Ethem Alpaydin: Introduction to Machine Learning, third edition. The MIT Press. ISBN 978-0-262-02818-9

Ordinary exam
Exam type:
D: Submission of written work with following oral, external (7-trinsskala)
Exam variation:
D2G: Submission of written work for groups with following oral exam supplemented by the work submitted. The group has a shared responsibility for the content of the report.
Exam description:

The group makes their presentations together and afterwards the students participate In the dialogue individually while the rest of the group is outside the room. The exam will last 20 min per student. The groups must consist of 2-3 persons.

Group Examination type: Mixed Exam 1.

Time and date