Valid from: Autumn 2026
Decided by: /Jonas Johansson
Date of establishment: 2026-01-15
Division: Mathematics
Course type: Third-cycle course
Teaching language: English
The aim of the course is to explore modern generative modeling approaches, with a particular focus on diffusion models, transformer architectures, large-scale architectures based on self-supervised learning and reinforcement learning from human feedback (RLHF), and parametric and implicit models for visual reconstruction and synthesis. A secondary aim is to further develop critical thinking and scientific creativity, enabling students to identify open problems and formulate new research directions beyond immediate incremental developments. The course emphasizes methodological understanding, conceptual clarity, and research judgment in rapidly evolving and computationally demanding areas of generative AI
Knowledge and Understanding
For a passing grade the doctoral student must
Competences and Skills
For a passing grade the doctoral student must
Judgement and Approach
For a passing grade the doctoral student must
The course covers the following topics: ● Diffusion models, transformer architectures, and static and temporal generative models ● Parametric models and implicit functions for reconstruction and synthesis, 3D visual modeling ● Generative and multimodal architectures based on self-supervised learning and reinforcement learning from human feedback (RLHF) Modern developments in deep learning and AI are driven both by large-scale empirically developed models and by rapidly advancing fundamental methodologies. The course adopts a critical methodological perspective, examining how to balance modeling complexity, empirical scale, theoretical structure, and experimental sufficiency under increasing computational, data, and organizational constraints. The course is intended for students who are already familiar with standard deep learning and generative modeling techniques and focuses on frontier-level concepts, open problems, and emerging research directions.
Scientific articles and research papers covering both foundational work and the current research frontier in generative modeling, computer vision, and machine learning.
Types of instruction: Lectures, seminars, self-study literature review
Examination formats: Oral exam, written report.
An individual or group essay and oral presentation on a selected topic, where the student/group expands on feasible future research directions. The examination is conceptually similar to developing a research agenda or exploratory proposal, broader in scope than a single paper and aimed at identifying promising questions, approaches, models, or research programs in an important and insufficiently understood domain.
The examination is designed to assess the student/group’s ability to critically synthesize literature, formulate original research directions, and reason about feasibility, scope, and methodological trade-offs.
Grading scale: Failed, pass
Examiner:
Assumed prior knowledge: The course is intended for advanced graduate students or other academics or professionals with a solid background in computer vision and machine learning. Prior research experience is expected; prior publications are beneficial but not required. The course is suitable for participants interested in deepening their research skills and engaging with frontier-level problems in generative AI.
Course coordinators: