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