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

Calculus 1, Statistics and Linear Algebra. Basic knowledge of a programming language (Python, MatLab, R etc.).

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

Individual oral exam, counts for 40%. Graded assessment.

Individual assignment, counts for 30%. Graded assessment.

Individual assignment, counts for 30%. Graded assessment.

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.

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 1:41:38 PM