Table of contents

The course is connected to the following study programs

Teaching language

English

Recommended previous knowledge

The students are expected to have basic knowledge of linear algebra, probability theory and calculus equivalent to MA-223 Statistikk, MA-178 Mathemetics 1 and MA-179 Mathematics 2.

Learning outcomes

Upon completion of the course, the students should

  • have a general knowledge of mathematics used in machine learning and artificial intelligence
  • understand how random variables are described
  • understand how the chain rule is used in machine learning
  • understand concepts including maximum likelyhood estimation, regression techniques, classification evaluation, and dimensional re-duction techniques
  • have a general knowledge of Mathematical Game Theory and Markov chains
  • be able to develop time dependent, deterministic, and stochastic state space functions

Contents

The course focuses on mathematical principles needed for practical machine learning tasks. This includes:

  • probability and information theory including random variables, chain rule of the conditional probabilities, and properties of mathematical functions commonly used on machine learning
  • topics from linear algebra essential for machine learning tasks
  • numerical computations including gradient descent and constrained optimization
  • core mathematical concepts in machine learning such as maximum likelyhood estimation, regression techniques, classification evaluation, and dimensional re-duction techniques
  • identifying and developing solutions for Mathematical Game theory
  • developing dynamical systems including but not limited to time dependent functions, deterministic and stochastic state space, and evolution rules
  • the theory and practice of Markov chains

Teaching and learning methods

The course is organized with a combination of lectures, assignments, paper studies, labs, and report writing. The tasks are done individually or in small groups with group supervision. The work load for the average student is approximately 200 hours.

Examination requirements

Compulsory Assignments: Information about the class assignments will be given in Canvas at the start of the course. Graded Pass/Fail

Students must pass the compulsory assignments in order to take the final written examination.

Examinations

Compulsory Assignments: Information about the class assignments will be given in Canvas at the start of the course. Graded Pass/Fail

Students must pass the compulsory assignments in order to take the final written examination.

Written exam, 3 hours. 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, if there are places available.

Admission Requirement if given as a free-standing course

Admission requirements for the course are the same as for the master’s programme in ICT.
Last updated from FS (Felles studentsystem) Oct. 15, 2026 7:58:36 AM