Generative AI (DAT560)
This course provides an in-depth understanding of generative AI, covering key concepts, techniques, and applications. It explores model development for generating text, images, and media, with advanced topics like multimodal AI and ethical considerations such as fairness and bias.
Course description for study year 2025-2026. Please note that changes may occur.
Course code
DAT560
Version
1
Credits (ECTS)
10
Semester tution start
Spring
Number of semesters
1
Exam semester
Spring
Language of instruction
English
Content
Learning outcome
Knowledge:
- Understand the fundamental concepts and principles of generative AI.
- Ability to identify the need and use generative AI in diverse domains and applications.
- Grasp the ethical challenges associated with generative AI, including issues of fairness and bias.
Skills:
- Develop and refine generative AI models for various applications, with an emphasis on practical implementation.
- Evaluate the effectiveness and impact of generative AI systems in different contexts.
- Design and build generative AI applications, considering the entire development lifecycle from concept to deployment.
General Competencies:
- Apply generative AI techniques to solve real-world problems in a variety of domains.
- Critically assess the societal implications of generative AI, particularly in terms of ethics and responsibility.
- Collaborate effectively with interdisciplinary teams to innovate in the field of generative AI
Required prerequisite knowledge
Recommended prerequisites
Exam
Project report + code + oral presentation and written exam
| Form of assessment | Weight | Duration | Marks | Aid |
|---|---|---|---|---|
| Project report + code + oral presentation | 2/5 | Letter grades | ||
| Written exam | 3/5 | Letter grades |
Project consisting of one large assignment (40% of the grade) done over 6 weeks. The project is to be performed in a group. The grade for the project will be based on the submitted program code, project report document and an oral hearing in groups of the submitted program code and report. Both parts must be done before final grade is given. If a student fails the project, she/he has to take this part next time the subject is lectured.The written exam (60% of the grade) will be digital (Inspera).Both exam units must be passed in order to receive a final grade in the course.
Coursework requirements
3 compulsory programming assignments ungraded (pass or fail) divided among fundamentals, LLMs and vision modules.
All programming exercises must be passed to attend for the written exam and to get project approved. Completion of mandatory lab assignments are to be made on time. Absence due to illness or for other reasons must be communicated as soon as possible to the laboratory personnel. One cannot expect that provisions for completion of the lab assignments at other times are made unless prior arrangements with the laboratory personnel have been agreed upon. Failure to complete the assigned labs on time or not having them approved will result in barring from taking the exam of the course. Manual approval of the assignments based on the demonstration of deeper understanding of the assignment solution is compulsory.