Generative AI
People
Course director
Description
This course provides a foundation in the principles and techniques underlying modern generative AI, with a focus on diffusion and flow matching models. Students will explore how state-of-the-art image and video generation systems are built and will examine recent advances and emerging research trends in the field.
Objectives
Upon completion of the course, students will:
- Understand the fundamental concepts behind modern generative AI models.
- Explain how diffusion and flow matching models are designed, trained, and evaluated.
- Analyze the strengths and limitations of different generative modeling approaches.
- Apply generative AI concepts to image and video generation tasks.
- Critically assess state-of-the-art models and current research developments in generative AI.
Teaching mode
In presence
Learning methods
Lectures, bi-weekly exercises and quizzes, semester project.
Examination information
Project work (20%)
Written exam (80%)
Education
- Master of Science in Artificial Intelligence, Lecture, Elective, 2nd year
- Master of Science in Informatics, Lecture, Elective, 1st year
- Master of Science in Informatics, Lecture, Elective, 2nd year
- Master of Science in Software & Data Engineering, Lecture, Elective, 2nd year
