Study plan for MAMN-INFMA Machine Learning, spring 2022

ECTS Credits

Two years of full-time study, where the normal workload for a full-time student is 60 credits for one academic year.



Language of Instruction




Objectives and content

A Master's degree in informatics with a specialization in machine learning focuses on developing computer programs that improve their performance based on empirical data. This type of self-learning programs are behind much of the recent breakthroughs in artificial intelligence.

The degree gives the candidate a good understanding on the theoretical aspects of machine learning as well as practical application of machine learning methods.

Required Learning Outcomes

On completion of the programme the candidate should have the following learning outcomes defined in terms of knowledge, skills and general competence:


The candidate

  • understands theoretical basis of machine learning
  • knows strengths and weaknesses of the main types of machine learning methods and can choose an appropriate one for problems at hand.


The candidate

  • can design and implement machine learning algorithms
  • can solve real-world problems using machine learning

General competence

The candidate

  • is able to work independently and in groups with others
  • has critical and analytical view of his/her own work and that of others.

Admission Requirements

The master programme builds on a Bachelor degree from the Department of Informatics, or a Bachelor degree and at least 80 ECTS of Mathematics and Informatics/Computer science.

Bachelor degrees from UiB that qualify: 

  • Bachelor degree from Department of Informatics, UiB (BAMN-BINF, BAMN-DSCI, BAMN-DSIK, BAMN-DTEK, BAMN-DVIT, BATF-IMØ, BAMN-INF)
  • Bachelor degree in Information and Communication Technology (IKT) with at least 15 ECTS (at least two courses for at least 7,5 ECTS each) of elective Mathematics (from autumn 2022 MAT101 will no longer count)
  • Bachelor degree in Cognitive Science with specialization in Informatics

Bachelor degrees that qualify 

  • Bachelor degree in Computing from HVL (Western Norway University of Applied Sciences)
  • Bachelor degree in Information Technology from HVL
  • Bachelor degree in Communication Systems from HVL

Other bachelor degrees might qualify if you have at least 80 ECTS of Mathematics and Informatics/Computer Science. Out of these 80 ECTS, at least 40 ECTS must be in Informatics/Computer science, and at least 15 ECTS (at least two courses with 7,5 ECTS each) must be in Mathematics. From autumn 2022 MAT101 and equivalent will no longer count. If both INF100 and INF109 are part of the Bachelor's degree, the applicant will only get credit for INF100. One course in Statistics can count towards Mathematics. MNF130 (and equivalent) counts as Informatics.

For international self-financing applicants:

The Master Programme is not available for international applicants residing outside of the Nordic countries, the European Union/EEA and Switzerland.

You also need to document:

Recommended previous knowledge

A bachelor¿s degree in informatics, computer science or a related field.

Good programming skills.

Strong mathematical background, especially linear algebra, calculus and probability and statistics.

Compulsory units

The master's programme consists of two components: Coursework of 60 credits and an individual research project (master's thesis) of 60 credits.

Courses: The courses INF234 and INF264 and 265 are compulsory. In addition, there are 30 credits of elective courses, chosen in agreement with the supervisor.

1 semester: INF234, INF264, one elective course

2- semester: INF265, two elective courses

3 semester: master thesis

4. semester. master thesis

Master's thesis: INF399 Master's thesis in informatics consist of 60 ECTS. The Master's thesis must be submitted within a deadline at the end of the fourth semester, 20 November or 1. June.

Recommended electives

Recommended elective courses:

30 ECTS in the Master's programme are elective and have to be chosen in agreement with the supervisor.

Courses INF367, INF368 are highly recommented. Other recommended courses includes: INF237, INF250, INF270, INF271, INF272 and STAT250.

Sequential Requirements, courses

The recommended sequence of the courses in the programme can be found under the heading ¿Compulsory units¿.

Study period abroad

You can plan study periods abroad in consultation with your supervisor as a part of the master agreement.

Teaching and learning methods

A combination of teaching and learning methods is used in the various courses, including lectures, hands-on exercises, and projects. You may find more information in the course description.

The Master¿s thesis is an independently scientific work, under supervision of an academic supervisor.

Assessment methods

The assessment methods in the courses are reports, written and oral examination. The assessment methods for each course are described in the course description.

The final step in the programme is an oral examination. The examination is held when the master¿s thesis is submitted, evaluated and approved.

The most common assessment methods in the courses are written and oral examination. The assessment methods for each course are described in the course description.

Grading scale

At UiB the grades are given in one of two possible grading scales: passed/failed and A to F.

The master¿s thesis will be graded A to F.

The grading scale for each course is given in the course description.


Machine learning is the core technology driving the artificial intelligence development. At the moment, there is lots of demand for skilled machine learning experts in wide-range of industry sectors. Examples include energy and financial sectors.

The degree also qualifies for PhD studies and opens a possibility to become a researcher in machine learning.


The programme will be evaluated according to the quality assurance system of the University of Bergen.

Administrative responsibility

The Faculty of Mathematics and Natural Sciences by the Department of Informatics, holds the administrative responsibility for the programme

Contact information

Please contact the student adviser for the programme if you have any questions: Studierettleiar@ii.uib.no

Phone: + 47 55 58 42 00