KIUA2004 Development I

    • Course code
      KIUA2004
    • Number of credits
      15
    • Teaching semester
      2027 Spring
    • Language of instruction and examination
      Norwegian/English
    • Campus
      Hamar
    • Required prerequisite knowledge

      Recommended: KIUA2000 Machine learning

Course content

This course provides a comprehensive introduction to the development of practical and useful AI and ML technologies. This course will also guide students on the gathering of requirements from general contexts. Students will be taught how to use these requirements to develop such AI and ML technologies. Through this course, students will also learn about integration readiness for such AI and ML technologies in relation to general contexts. Students will continue to develop general skills in project planning and knowledge sharing as part of this course.

Learning outcome

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

Knowledge

Students have:

  • Knowledge about the development of practical and useful AI and ML technologies
  • Knowledge about requirements from general contexts for developing such AI and ML technologies
  • Knowledge about preparing such AI and ML technology integration readiness in relation to general contexts
Skills

Students can:

  • Understand and develop practical and useful AI and ML technologies
  • Systematically gather requirements from general contexts that can guide the development of such AI and ML technologies
  • Develop such AI and ML technologies in terms of integration readiness in generalised contexts
General competence

Students can:

  • Develop practical and useful AI and ML technologies for real-world problems
  • Develop integration-ready AI and ML technologies and tools in relation real-world contexts
  • Work in collaborative projects through different activities
Working and teaching methods

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

Compulsory activities
  • 2 individual pieces of required coursework, compulsory physical attendance on campus.
  • Attendance in classes/teaching sessions is mandatory, where physical attendance on campus is required. There is an 80% attendance requirement in teaching sessions and a 100% attendance requirement in specific learning activities. This is in accordance with the teaching plan for each course in the programme of study.
Examination
Form of assessmentGrading scaleGroupingDuration of assessmentSupport materialsProportionComments
Written assignment
ECTS - A-F
Group
100%
Form of assessment
  • 1 project-based group assignment

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

Permitted examination support material:

  • 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
Course name in Norwegian Bokmål: 
Utvikling I
Faculty
Faculty for Film, TV and Games
Department
Department of Game Development - The Game School
Area of study
Matematisk-naturvitenskapelige fag/informatikk
Programme of study
Bachelor i kunstig intelligens - utvikling og anvendelse
Course level
Intermediate course, level II (200-LN)