Generative Models
Autumn Quarter
Instructor: Niels Nygaard
Syllabus
The course provides an introduction to Generative AI, covering both the theoretical foundations and practical applications of generative models. Students will learn about various generative models, including Variational Autoencoders (VAEs), Energy Based Models, DiKusion Models and more. They will gain hands-on experience in implementing and training generative models for tasks such as image generation, text generation, and data synthesis.
Prerequisites: Linear algebra, calculus, probability theory (such as FINM 34000 - Probability and Stochastic Processes), and basic programming skills in Python.
This course counts towards the Machine Learning and AI concentration.