KIUA1001 Machine learning I

    • Number of credits
      10
    • Teaching semester
      2025 Spring
    • Language of instruction
      Norwegian/English
    • Campus
      Hamar
    • Required prerequisite knowledge

      None

Course content

The course provides students with a comprehensive understanding of the concepts and theories that form the basis for machine learning, including different types of machine learning and specific machine learning methods. The course also aims to develop technical skills in software engineering and computer science, as well as communication and problem-solving skills. Students will be able to analyse, manipulate and interpret data using machine learning technology, apply machine learning technologies to solve real-world problems with a holistic world view, evaluate the implications of underlying causalities derived from system thinking and system analysis and work independently or as part of a team to develop machine learning models.

Learning Outcome

Upon successfully passing the course, students will have achieved the following learning outcomes:

Knowledge

The student will have:

  • knowledge of the understanding of the concepts and theories that form the basis for machine learning
  • knowledge of different types of basic machine learning algorithms
  • knowledge of the details of specific machine learning methods
  • knowledge of basic principles for system thinking, system analysis and causality mapping
Skills

The student will be able to

  • formulate learning questions and concepts relating to representation, overfitting and generalisation
  • analyse, manipulate and interpret data using machine learning technology
  • analyse and structure complex systems and sort when causality is required to guide the use of AI algorithms
  • identify suitable machine learning approaches for genuine applications based on the application of knowledge of the principles of system thinking, system analysis and causality mapping
General competence

The student will be able to

  • map complex systems and understand how different variables (data) are linked
  • apply machine learning technology to solve fundamental real-world problems
  • work independently and as part of a team to develop machine learning models
  • apply system thinking and analysis of problems and challenges associated with artificial intelligence
Teaching and working methods

The course comprises a combination of lectures, practical exercises, independent study and academic supervision.

Required coursework
  • Two individual compulsory coursework requirements
  • Participation in teaching and laboratory exercises in accordance with the course curriculum
Form of assessment
  • One project-based assignment, individually or in a group

 

The assignment is assessed using a grading scale from A-F, where E is the lowest passing grade.

Students are able to choose which language to use for their examination. The available options are Norwegian Bokmål, Nynorsk and English.

 

Permitted aids

  • Literature
  • All printed and written resources
  • Any use of AI-generated text and content must be clarified with the lecturer, clearly labelled and academically justified in the submission
Assessments
Form of assessmentGrading scaleGroupingDuration of assessmentSupport materialsProportionComment
Written assignment
ECTS - A-F
Group/Individual
  • All
100
Any use of AI-generated text and content must be clarified with the lecturer, clearly labelled and academically justified in the submission.

Reading list

Faculty
Faculty of Audiovisual Media and Creative Technologies
Department
Department of Game Development - The Game School