KIUA1001 Machine learning I
- Course codeKIUA1001
- Number of credits10
- Teaching semester2025 Spring
- Language of instructionNorwegian/English
- CampusHamar
- Required prerequisite knowledge
None
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:
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
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
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
The course comprises a combination of lectures, practical exercises, independent study and academic supervision.
| Form of assessment | Grading scale | Grouping | Duration of assessment | Support materials | Proportion | Comment |
|---|---|---|---|---|---|---|
Written assignment | ECTS - A-F | Group/Individual |
| 100 | Any use of AI-generated text and content must be clarified with the lecturer, clearly labelled and academically justified in the submission. |
- 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