392195 Vertiefung Neuronale Netze (V) (WiSe 2025/2026)
Contents, comment
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This lecture will consider various unsupervised and instance-based learning approaches before turning back to deep neural networks. In the exercises will replicate various classical applications of neural network methods to interesting real-world problems.
- Topological Maps: Self-Organizing Maps, Growing Neural Gas, Hyperbolic SOM, Local Linear Maps
- Mixture Models: Gaussian Mixture Models, Gaussian Mixture Regression, Gaussian Processes
- Recurrent Neural Nets: Dynamics, Stability, applications: Hopfield net, CLM, ...
- Graph Networks: Processing Structural Information
- Generative Models: Generative Adversarial Networks (GAN), Actor-Critic, Variational Autoencoders
- Deep Reinforcement Learning
The lecture will be given in English language if desired by the audience.
Slides will be in English in any case.
Requirements for participation, required level
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recommended prerequisites:
- Machine Learning basics
- Neural Networks basics
External comments page
Teaching staff
Dates ( Calendar view )
| Frequency | Weekday | Time | Format / Place | Period | |
|---|---|---|---|---|---|
| weekly | Mi | 8-10 | X-E0-230 | 13.10.2025-06.02.2026 | Die Vorlesung findet von 08:30 - 10:00 Uhr statt! |
Subject assignments
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- Last update basic details/teaching staff:
- Friday, August 1, 2025
- Last update times:
- Wednesday, August 13, 2025
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- Wednesday, August 13, 2025
- Type(s) / SWS (hours per week per semester)
- lecture (V) / 2
- Language
- This lecture is taught in english
- Department
- Faculty of Technology
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