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Learning Generative Models

(in German: Learning Generative Models )

Module-ID: FIN-INF-140016
Link: LSF
Responsibility: Sebastian Stober
Lecturer: Sebastian Stober
Classes:
  • Lecture Learning Generative Models
  • Exercise class Learning Generative Models
 
Applicability in curriculum: - M.Sc. INF: Informatik
- M.Sc. INGINF: Informatik
- M.Sc. WIF: Informatik
- M.Sc. DKE: Learning Methods and Models for Data Science
- M.Sc. DE: Methoden der Informatik
- M.Sc. VC: Computer Science

Abbreviation

LGM

Credit Points

6

Semester

Winter

Term

ab 1.

Duration

1 Semester

Language

english

Level

Master

Intended learning outcomes:
Students can ..

  • confidently apply generative models to develop a solution for a given problem
  • follow recent publications on generative models and critically assess their contributions
  • formulate hypotheses and design & conduct experiments with generative models to validate them
  • document progress & design decisions for reproducibility and transparency

Content:

  • Training methods & architectures for generative models, in particular energy-based models (e.g. Restricted Boltzmann Machines, RBMs)
  • autoregressive models
  • variational learning and Generative Adversarial Nets (GANs)
  • flow-based models
  • diffusion models

Workload:
56 contact hours + 124h self study

Pre-examination requirements: Type of examination: Teaching method / lecture hours per week (SWS):

  • Oral exam (necessary preliminary work for admission to the examination is announced in the first week of the course)
  • Schein (necessary preliminary work for admission to the examination is announced in the first week of the course)

  • 2 SWS lecture
  • 2 SWS exercise class
Prerequisites according to examination regulations: Recommended prerequisites:

keine

Introduction to Deep Learning or Deep Learning für Ingenieure (required)

Media: Literature:

Ian Goodfellow, Yoshua Bengio & Aaron Courville: “Deep Learning”, MIT Press, 2016.

Comments:

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