392173 Vertiefung Neuronale Netze (V) (WiSe 2021/2022)

Contents, comment

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

recommendd prerequisites:
- Machine Learning basics
- Neural Networks basics

External comments page

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

Teaching staff

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Module Course Requirements  
39-M-Inf-VNN Vertiefung Neuronale Netze Vertiefung Neuronale Netze Ungraded examination
Graded examination
Student information

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Registered number: 27
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WS2021_392173@ekvv.uni-bielefeld.de
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22 Students to be reached directly via email
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Last update basic details/teaching staff:
Wednesday, June 16, 2021 
Last update times:
Wednesday, October 13, 2021 
Last update rooms:
Wednesday, October 13, 2021 
Type(s) / SWS (hours per week per semester)
V / 2
Language
This lecture is taught in english
Department
Faculty of Technology
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294419996