1. Introduction to Artificial Intelligence (AI). Languages of AI. Introduction to PROLOG.
2. Problem solving strategies. Expert System (ES). Describe the characteristics, features, structures, limitations, and benefits of ES.
3. Describe the various methods of knowledge representation and build simple rule-based knowledge bases. Describe the various methods of inference. Conduct manual backward and forward chaining inferences.
4. Artificial Neural Networks.
5. The most common models like Backpropagation multilayered.
6. Recurrent multilayered nets.
7. Kohonen, Counterpropagation nets are introduced.
8. Hopfield, BAM and ART nets are introduced.
9. Object-oriented model of all mentioned types of neural networks.
10. Expert System and Neural Network. Applications of Neural Network in Imaging Processing.
2. Problem solving strategies. Expert System (ES). Describe the characteristics, features, structures, limitations, and benefits of ES.
3. Describe the various methods of knowledge representation and build simple rule-based knowledge bases. Describe the various methods of inference. Conduct manual backward and forward chaining inferences.
4. Artificial Neural Networks.
5. The most common models like Backpropagation multilayered.
6. Recurrent multilayered nets.
7. Kohonen, Counterpropagation nets are introduced.
8. Hopfield, BAM and ART nets are introduced.
9. Object-oriented model of all mentioned types of neural networks.
10. Expert System and Neural Network. Applications of Neural Network in Imaging Processing.