392195 Vertiefung Neuronale Netze (V) (WiSe 2025/2026)

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.

Teilnahmevoraussetzungen, notwendige Vorkenntnisse

recommended prerequisites:
- Machine Learning basics
- Neural Networks basics

Externe Kommentarseite

http://ni.www.techfak.uni-bielefeld.de/teaching/vertiefung-neuronale-netze