Generative Artificial Intelligence
About the course
The course can be taken as a singular master's-level course.
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.
Admission requirements
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.
Prerequisites
Anbefalte forkunnskaper
INF-0102 Computational Programming, INF-1049 Introduction to computational programming, INF-1400 Object-oriented programming
Credit reduction
If you pass the examination in this course, you will get an reduction in credits (as stated below), if you previously have passed the following courses:
- INF-8606 Generative AI for health and life sciences 5 ects
Teaching methods
Language of instruction and examination
EnglishSchedule
The schedules are normally finalized and published well in advance of the start of the semester, often a few weeks beforehand. This gives students the opportunity to organize their studies and prepare for upcoming activities.
It is recommended to check the schedule regularly, as changes may occur.
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.
Examination
| Oral exam | Duration: 1 Hours Weighting: 2/5 |
Grade: A–E, fail F |
| Assignment | Hand in: 01.06.2026 14:00 Weighting: 3/5 |
Grade: A–E, fail F |
To take an examination, the student must have passed the following coursework requirements
| Mandatory assignment(s) | Grade: Approved – not approved |
Everything you need to know about before, during, and after the exam; registration, absence, appeals, and diplomas: UiT Exams homepage
More info about the coursework requirements
Two assignments. Students must upload videos in Canvas of their results.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
More info about the oral exam
The oral exam is a group presentation with same grade for all the group members.Re-sit examination
A re-sit exam will not be held.Info about the weighting of parts of the examination
Report, counting 60%
Oral group exam, counting 40%
