spring 2026
INF-3600 Generative Artificial Intelligence - 10 ECTS
Admission requirements
Higher Education Entrance Requirement + Bachelor's degree in Computer Science or similar education. The Bachelor degree must contain a specialization in Computer Science worth the equivalent of not less than 80 ECTS credits. Application code: 9371 - Singular courses at master's level.Course content
The course will cover Large Language Models (LLM), Generative AI for images, in addition to other modalities like sound. Applied aspects such as continuous integration in production systems will be given attention.
Some emphasis will be put on creating AI tools, which could lead to possible startup ideas.
Objectives of the course
Knowledge - The student has ...
- Knowledge about principles, models and ethics of Generative AI in general.
- Knowledge about various tuning and augmentation mechanisms of LLMs.
- Knowledge about diffusion models for image generation, and how LLMs can be utilized for specialized industry use-cases.
- General insight into the transformer model and the principles of attention.
- Knowledge about the limitations of Generative AI such as bias and hallucinations.
Skills - The student ...
- Has the ability to fine-tune, customize and interact with LLMs.
- Has the ability to design systems for image generation based on diffusion and variational autoencoders.
- Can set up machine learning pipelines involving Generative AI and operationalize such systems.
General competence - The student has ...
- Knowledge about basic design of Generative AI experiments and operations.
- Competence about how to evaluate, implement and validate solutions (or systems/applications?) based on Generative AI.
Information to incoming exchange students
This course is available for inbound exchange students.
This course is open for inbound exchange student who meets the admission requirements, including prerequisites. Please see the Admission requirements" and the "Prerequisite" sections for more information.
Do you have questions about this module? Please check the following website to contact the course coordinator for exchange students at the faculty: https://en.uit.no/education/art?p_document_id=510412.
Schedule
Examination
| Examination: | Weighting: | Duration: | Grade scale: |
|---|---|---|---|
| Oral exam | 2/5 | 1 Hours | A–E, fail F |
| Assignment | 3/5 | A–E, fail F | |
Coursework requirements:To take an examination, the student must have passed the following coursework requirements: |
|||
| Mandatory assignment(s) | Approved – not approved | ||
More info about the assignment
Project report done in groups with associated code (= home exam). The duration of the group project will be 8 weeks. The evaluation of the group project is individual and the group have to state their individual contribution in it.
The evaluation will be based on a project the students will develop during the course. Here, the students will conduct a practical experiment in which they perform and analyze one or more Generative AI techniques discussed in the course. The evaluation will be performed based on the report and the associated code. The students will be assessed according to the following criteria
- Problem and theory (fundament, insight, objectives, own contribution)
- Methods (ability, process, effort, independence)
- Results and discussion (perspective, result, analysis, performance vs objectives)
- Presentation (structure, language, quality
- About the course
- Campus: Tromsø | Bodø |
- ECTS: 10
- Course code: INF-3600
- Responsible unit
- Institutt for informatikk
- Contact persons
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- Earlier years and semesters for this topic