392186 Vertiefung Neuronale Netze (V) (WiSe 2023/2024)
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 |
|---|
Subject assignments
| Module | Course | Requirements | |
|---|---|---|---|
| 39-M-Inf-AI-adv-foc_ver1 Advanced Artificial Intelligence (focus) | Advanced Artificial Intelligence (focus): Vorlesung | Graded examination
|
Student information |
| 39-M-Inf-VNN Vertiefung Neuronale Netze | Vertiefung Neuronale Netze | Ungraded examination
Graded examination |
Student information |
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- Last update basic details/teaching staff:
- Wednesday, January 17, 2024
- Last update times:
- Friday, August 11, 2023
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- Friday, August 11, 2023
- 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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