Table of contents

The course is connected to the following study programs

Teaching language

Norwegian or English

Recommended previous knowledge

MA-166

A good understanding of linear algebra and calculus will be an advantage.

Learning outcomes

On successful completion of the course, the student should be able to

  • the most important aspects of supervised learning
  • build, estimate and interpret linear and non-linear models
  • create and interpret basic models for classification of data
  • use a programming language to apply machine learning techniques on large data sets

 

 

Contents

Multiple regression. Non-linear transformations of data. Confidence intervals for estimation of parameters. Logistic regression. Support vector machines.

 

Teaching and learning methods

Lectures, work in small groups and compulsory assignments. Computer lab. If needed, the course is taught in English. The course has an expected workload of around 200 hours.

Examination requirements

Required assignments must be approved, see Canvas for more information.

Examinations

A 5-hour written examination. Graded assessment.

Student evaluation

The study programme manager, in consultation with the student representative, decides the method of evaluation and whether the courses will have a midterm- or end of term evaluation, see also the Quality System, section 4.1. Information about evaluation method for the course will be posted on Canvas.

Offered as a free-standing course

Ja

Reduction of Credits

This course’s contents overlap with the following courses. A reduction of credits will occur if one of these courses is taken in addition:

Course Reduction of Credits
MA-202 – Statistics 2. 7.5
MA-441 – Statistics for Primary School Teachers. 7.5
MA-445 – Statistics for Secondary School Teachers. 7.5
MA-225 – Statistical machine learning. 7.5
Last updated from FS (Felles studentsystem) Oct. 15, 2026 6:17:27 AM