Neural Networks

Lecture Slides

  1. Biological Background
  2. Threshold Logic Units (TLU)
  3. Training TLUs
  4. General Artificial Neural Networks
  5. Multi Layer Perceptrons (MLP)
  6. Regression
  7. Training MLPs
  8. Sensitivity Analysis
  9. Deep Learning
  10. Radial Basis Function (RBF) Networks
  11. Training RBF Networks
  12. Learning Vector Quantization
  13. Self-organizing Maps
  14. Hopfield Networks and Boltzmann Machines
  15. Recurrent Neural Networks
  16. Neuro Fuzzy Systems
  17. Full Lecture

Exercise Sheets

  1. Threshold Units, Simple Neural Networks
  2. Update Order, Function Approximation
  3. Regression, Gradien Descent, Backpropagation, Dropout
  4. Radial Basis Functions (RBF), RBF Networks
  5. Competitive Learning, Self-organizing Maps, Hopfield Networks
  6. Deep Learning, Convolutional Neural Networks, Recurrent Neural Networks

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