KIUA2007 Translational artificial intelligence II

    • Course code
      KIUA2007
    • Number of credits
      15
    • Teaching semester
      2027 Autumn
    • Language of instruction and examination
      Norwegian/English
    • Campus
      Hamar
    • Required prerequisite knowledge

      Recommended: KIUA2006 Translational artificial intelligence I

Course content

This course will provide a comprehensive introduction to domain-specific state-of-the-art AI and ML technologies. Students will be taught about the needs of the various domains that can be addressed using such AI and ML technologies. Some of these domains include healthcare, finance, management, creative industries, natural sciences, education, governance, emergency preparedness management and many other current and emerging domains. The course will also help students critically assess the relevance of such AI and ML technologies and tools for the various domains. 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 use and adjustment of domain-specific state-of-the-art AI and ML technologies
  • Knowledge about needs in various domains for such AI and ML technologies
  • Knowledge about AI and ML technology integration challenges in various domains
Skills

Students can:

  • Understand and be able to use and adjust domain-specific state-of-the-art AI and ML technologies
  • Identify needs in different domains that can be met using such AI and ML technologies
  • Use such AI and ML technologies and tools for various domains (such as healthcare, finance, management, creative industries, natural sciences, education, governance, emergency preparedness management, and many other current and new domains.)
  • Critically assess the relevance of such AI/ML technologies and tools for various domains (such as healthcare, finance, management, creative industries, natural sciences, education, governance, emergency preparedness management and many other current and emerging domains).
General competence

Students can:

  • Use domain-specific state-of-the-art AI/ML technologies for real-world problems in these domains
  • Critically assess the relevance of such AI/ML technologies and tools in 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

Reading list

Course name in Norwegian Bokmål: 
Translasjonell kunstig intelligens II
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
Third-year courses, level III (300-LN)