Course

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.


Dette er emnebeskrivelsen for studieåret 2025-2026

Fakta

Emnekode

DAT560

Vekting (stp)

10

Semester undervisningsstart

Spring

Undervisningsspråk

English

Antall semestre

1

Vurderingssemester

Spring

Content

This course offers a deep understanding of generative artificial intelligence (AI) fundamentals, covering foundational concepts, key techniques, and a wide range of applications. Students will learn about the core principles that underpin generative AI, including the development and evaluation of models capable of creating text, images, and other forms of media. The course also introduces advanced topics, such as the integration of text and visual data, and the application of generative AI across different domains like medicine, law, and creative industries. Ethical considerations, such as fairness and bias, are emphasized throughout the course, ensuring students are prepared to build responsible AI systems.

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

Forkunnskapskrav

Ingen

Anbefalte forkunnskaper

Introduction to Programming (DAT120), Probability and Statistics 1 (STA100)
Python, Jupyter notebooks, pandas, scikit-learn, pytorch, tensorflow.

Exam

Vilkår for å gå opp til eksamen/vurdering

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.

Method of work

4 hours lectures/exercises and 2 hours of guided programming exercises and project. Programming exercises requires additional non-guided work effort.

Åpent for

Admission to Single Courses at the Faculty of Science and Technology
Data Science - Master of Science Degree Programme Computer Science - Master of Science Degree Programme Cybernetics and Applied AI - Master of Science Degree Programme
Exchange programme at Faculty of Science and Technology

Emneevaluering

There must be an early dialogue between the course supervisor, the student union representative and the students. The purpose is feedback from the students for changes and adjustments in the course for the current semester.In addition, a digital course evaluation must be carried out at least every three years. Its purpose is to gather the students experiences with the course.
The course description is retrieved from FS (Felles studentsystem). Version 1