Lectures:
1. Neuron models. Neurons of the 1st generation. Neurons of the 2nd generation - Perceptron.
2. Neuron adaptation. Hebb's Algorithm. Widrwo-Hoff learning of linear neuron.
3. Multilayered architectures. Backpropagation and its parametric modification.
4. Implemenation of neuron with interval based excitation. Generalized Backpropagation.
5. Recurrent neural networks.
6. Kohonen learning and Self Organized Maps. Counter-propagation.
7. Hopfield networks. Boltzmann Machine. Bidirectional Associative Memory.
8. Využití Hopfieldových sítí v úlohách s omezujícími podmínkami.
9. Adaptive Resonance Theory.
10. Usage of genetic algorithm for neural netwok adaptation. Object-Oriented Design of Neural Networks.
Exercises (PC classroom):
1. Neuron models. Neurons of the 1st generation. Neurons of the 2nd generation - Perceptron.
2. Neuron adaptation. Hebb's Algorithm. Widrwo-Hoff learning of linear neuron.
3. Multilayered architectures. Backpropagation and its parametric modification.
4. Implemenation of neuron with interval based excitation. Generalized Backpropagation.
5. Recurrent neural networks.
6. Kohonen learning and Self Organized Maps. Counter-propagation.
7. Hopfield networks. Boltzmann Machine. Bidirectional Associative Memory.
8. Využití Hopfieldových sítí v úlohách s omezujícími podmínkami.
9. Adaptive Resonance Theory.
10. Usage of genetic algorithm for neural netwok adaptation. Object-Oriented Design of Neural Networks.
1. Neuron models. Neurons of the 1st generation. Neurons of the 2nd generation - Perceptron.
2. Neuron adaptation. Hebb's Algorithm. Widrwo-Hoff learning of linear neuron.
3. Multilayered architectures. Backpropagation and its parametric modification.
4. Implemenation of neuron with interval based excitation. Generalized Backpropagation.
5. Recurrent neural networks.
6. Kohonen learning and Self Organized Maps. Counter-propagation.
7. Hopfield networks. Boltzmann Machine. Bidirectional Associative Memory.
8. Využití Hopfieldových sítí v úlohách s omezujícími podmínkami.
9. Adaptive Resonance Theory.
10. Usage of genetic algorithm for neural netwok adaptation. Object-Oriented Design of Neural Networks.
Exercises (PC classroom):
1. Neuron models. Neurons of the 1st generation. Neurons of the 2nd generation - Perceptron.
2. Neuron adaptation. Hebb's Algorithm. Widrwo-Hoff learning of linear neuron.
3. Multilayered architectures. Backpropagation and its parametric modification.
4. Implemenation of neuron with interval based excitation. Generalized Backpropagation.
5. Recurrent neural networks.
6. Kohonen learning and Self Organized Maps. Counter-propagation.
7. Hopfield networks. Boltzmann Machine. Bidirectional Associative Memory.
8. Využití Hopfieldových sítí v úlohách s omezujícími podmínkami.
9. Adaptive Resonance Theory.
10. Usage of genetic algorithm for neural netwok adaptation. Object-Oriented Design of Neural Networks.