Generative Models

This course prepares students to explain the concepts of generative modelling and data distributions, implement VAE, GAN and diffusion models, and evaluate the output quality and ethical implications of generative artificial intelligence. Study materials: Data Management (DM), Artificial Intelligence (AI).

Topics

1. Concepts of generative modelling
2. Variational autoencoders (VAE)
3. Generative adversarial networks (GAN)
4. Diffusion models
5. Generative models for text and images
6. Evaluation of generative model output quality
7. Ethics of generative artificial intelligence

Each course outcome derives from one programme outcome.

1. CPMK-AIDE-02

   Students are able to build an applied decision basis by optimising computing performance through simultaneous processing, and apply the principles of computing ethics

   CPLP03
2. CPMK-AIDE-01

   Students are able to design and implement advanced artificial intelligence modelling to analyse diverse large-volume data through the management of structured data storage infrastructure.

   CPLP03

CPL10

Apply a scientific approach to design, carry out and analyse IT solutions and document them in systematic reports.

CPLP03

Develop advanced artificial intelligence models and manage big data, and build applied decision support with attention to optimal performance and computing ethics.