Table of contents

The course is connected to the following study programs

  • Industrial Mathematics, Bachelor's Programme

Teaching language

Norwegian. English when needed.

Recommended previous knowledge

MA-183, MA-185, MA-186, INF120

Learning outcomes

Upon successful completion of the course, the student will be able to

  • Understand the most important principles of supervised learning and how to evaluate them
  • Build, estimate and interpret linear and non-linear regression models.
  • Build and interpred simple models for classification of data
  • Use a programming language to apply machine learning techniques for large data sets.

Contents

Statistical methods in machine learning. Multipple regression. Non-linear transformation of data. Confidens intervals in parameter estimation. Logistic regression. Support vector machines.

Teaching and learning methods

Lectures, group work, mandatory hand-ins. Estimated workload of the course is 267 hours.

Examination requirements

Approved mandatory hand-ins. See Canvas for more information.

Examinations

5-hours written exam under supervision. The exam is graded.

Student evaluation

The person responsible for the course decides, in cooperation with student representative, the form of student evaluation and whether the course is to have a midway or end of course evaluation in accordance with the quality system for education, chapter 4.1.

Offered as a free-standing course

Yes. Subject to availability or capacity.

Admission for external candidates

No

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-217 – Statistical Machine Learning. 7.5
Last updated from FS (Felles studentsystem) Oct. 15, 2026 10:20:52 AM