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Week - 1 |
What is the artifical neural network (ANN)?: Biologic neural networks Activation functions, Architectures of Artificial Neural Network Adjusting weights, Using fields Mc Culloch-Pitts Neurons. |
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Week - 2 |
Simple ANN algotihms for patern clasification: Linear separability Hebb Rule (network), Hebb learning algorithm, Hebb network for logic functions, Application of pattern recognation with Hebb Networks |
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Week - 3 |
Simple Perceptron: Architecture Perceptron learning algorithm, Perceptron application to logic functions, Perceptron application to pater recognation, |
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Week - 4 |
ADALINE (Adaptive Linear Neuron) network: Delta rule ADALINE architecture and training algorithm application algorithm of ADALINE ADALINE for logical functions, explaining delta rule. |
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Week - 5 |
Relationship basic neural network model with statistical models as regression and pattern recognition examples. |
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Week - 6 |
Pattern recognition: Advanced Hebb and delta rule external product Examples and pattern recognition. |
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Week - 7 |
Pattern recognition: otorelationship networks and their examples storing capability. |
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Week - 8 |
Iterative otorelationship network and applications Discrete Hopfield network BAM network. |
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Week - 9 |
Multilayer perceptron Backpropagation training algorithm. |
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Week - 10 |
Generalized Delta Rule (GDR). |
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Week - 11 |
Comparison of Multilayer Backpropagation networks and Nonlinear Regression Models. |
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Week - 12 |
Consideration of Homeworks and Projects |
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Week - 13 |
Consideration of Homeworks and Projects |
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Week - 14 |
Consideration of Homeworks and Projects |
Eskisehir Technical University Info Package 
Turkish 
English