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Vorlesung: 03-IMAP-DGM Deep Generative Models - Details

Vorlesung: 03-IMAP-DGM Deep Generative Models - Details

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Veranstaltungsname Vorlesung: 03-IMAP-DGM Deep Generative Models
Untertitel
Veranstaltungsnummer 03-IMAP-DGM
Semester WiSe 2025/2026
Aktuelle Anzahl der Teilnehmenden 59
Heimat-Einrichtung Informatik
Veranstaltungstyp Vorlesung in der Kategorie Lehre
Nächster Termin Montag, 08.12.2025 10:00 - 12:00, Ort: CART Rotunde - 0.67
Art/Form
Englischsprachige Veranstaltung Ja
ECTS-Punkte 6

Modulzuordnungen

Kommentar/Beschreibung

Schwerpunkt: IMA-AI
https://lvb.informatik.uni-bremen.de/imap/03-imap-dgm.pdf

Dieser Kurs findet auf Englisch statt/The course is in English.

Learning Outcome:
In many applications of AI and machine learning the goal transcends a mere decision making process. In general, decisions should be grounded in estimates of model uncertainty, understand the underlying training distribution through learned representations, or they need to rely on data imputation. Generative models aim to address these factors, while also providing the means to further synthesize data, compress it, as well as estimate density and discover structure. Upon successful completion of the course, students will have gained an understanding of why generative models are critical, independently of whether the goal is to arrive at a decision or to generate data. They will learn the different ways to design generative models, from mixture models and probabilistic circuits, to variational, adversarial and flow-based models, all the way to large-scale models that are being referred to as Gen AI. In the process they will be equipped with the necessary
mathematical skills to understand the underlying technical foundations and engage with potential
applications.

Course Content:
* Learning and probabilistic inference
* From Gaussian mixture models to probabilistic circuits
* Latent variable models and variational inference
* Generative adversarial networks
* Flow models and change of variables
* Energy-based generative models and diffusion
* Autoregression, large language modeling, and GenAI
* Applications of generative models