Generative Multimedia
| Lecturer (assistant) | |
|---|---|
| Number | 0000001215 |
| Type | lecture with integrated exercises |
| Duration | 4 SWS |
| Term | Sommersemester 2026 |
| Language of instruction | English |
| Position within curricula | See TUMonline |
| Dates | See TUMonline |
- 10.07.2026 08:15-09:45 0406, Seminarraum
- 10.07.2026 09:15-10:45 0943, Praktikum
- 10.07.2026 15:00-16:30 0406, Seminarraum
- 17.07.2026 08:15-09:45 0406, Seminarraum
- 17.07.2026 09:15-10:45 0943, Praktikum
- 17.07.2026 15:00-16:30 0406, Seminarraum
Admission information
Description
- Introduction to probability theory, generative modeling, and Deep Learning
- Maximum Likelihood Learning and Sampling techniques
- Theory and applications of Autoregressive models, Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Energy-based models and normalizing flows, Transformers, Diffusion models, Multimodal models (language-image), etc.
- Creative applications: image synthesis, text-to-image generation, and music composition
- Tiny Generative Models for Edge Devices (challenges and techniques for running generative models on resource-constrained hardware, model compression, quantization, and software frameworks
- Maximum Likelihood Learning and Sampling techniques
- Theory and applications of Autoregressive models, Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Energy-based models and normalizing flows, Transformers, Diffusion models, Multimodal models (language-image), etc.
- Creative applications: image synthesis, text-to-image generation, and music composition
- Tiny Generative Models for Edge Devices (challenges and techniques for running generative models on resource-constrained hardware, model compression, quantization, and software frameworks
Prerequisites
Probability theory, Linear Algebra, Optimization
Teaching and learning methods
Lectures introduce theoretical concepts, providing context, and discussing real-world applications. Tutorial sessions consist of hands-on implementation, where students apply theoretical knowledge to coding assignments. Tutorial sessions may also include derivations that could not be covered in depth during the lecture.
Teaching methods
- Presentation slides with theory, applications, and discussions on research papers
- Interactive coding sessions
- Guest lectures from industry experts (if available)
Learning methods
- Tutorials with programming assignments to build and train generative models
- Essays summarizing and critiquing cutting-edge research papers
Teaching methods
- Presentation slides with theory, applications, and discussions on research papers
- Interactive coding sessions
- Guest lectures from industry experts (if available)
Learning methods
- Tutorials with programming assignments to build and train generative models
- Essays summarizing and critiquing cutting-edge research papers