Relevant concepts from probability theory and estimation Introduction to synthesis problems and generative models
Principles of synthesis versus classification
Regression versus probabilistic modelling
Modelling goals and evaluation
Mixture density networks (MDNs)
Autoregression and large language models (LLMs)
Normalising flows
Variational autoencoders (VAEs)
Diffusion models and flow matching
Generative adversarial networks (GANs)
Subjective evaluation
Hybrid approaches
Recent developments
Ethical aspects of generative AI
FDD3601 Deep Generative Models and Synthesis 7.5 credits
Information per course offering
Course offerings are missing for current or upcoming semesters.
Course syllabus as PDF
Please note: all information from the Course syllabus is available on this page in an accessible format.
Course syllabus FDD3601 (Autumn 2025–)Headings with content from the Course syllabus FDD3601 (Autumn 2025–) are denoted with an asterisk ( )
Content and learning outcomes
Course contents
Intended learning outcomes
After passing the course, the students should be able to:
characterise synthesis problems, deep generative methods, and their applications
distinguish different objectives, performance measures, and common problems with generative modelling
describe the relation between deep generative models and regression-based methods
train and tune deep generative models on different datasets
evaluate generative models objectively and subjectively
discuss ethical aspects of particular relevance to generative AI
in order to
be able to judiciously use deep generative modelling to solve problems in industrv and/or academia.
Literature and preparations
Specific prerequisites
No information inserted
Literature
You can find information about course literature either in the course memo for the course offering or in the course room in Canvas.
Examination and completion
Grading scale
P, F
Examination
- LAB1 - Laboratory work, 7.5 credits, grading scale: P, F
Other requirements for final grade
The final grade is determined by accumulating the grades in individual exercises.
Examiner
Ethical approach
- All members of a group are responsible for the group's work.
- In any assessment, every student shall honestly disclose any help received and sources used.
- In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.
Further information
Course room in Canvas
Registered students find further information about the implementation of the course in the course room in Canvas. A link to the course room can be found under the tab Studies in the Personal menu at the start of the course.
Offered by
Education cycle
Third cycle