1. SylabUZ
2. Faculty of Computer Science, Electrical Engineering and Automatics
3. winter term 2022/2023
4. Computer Science - First-cycle Erasmus programme
5. Elements of artificial intelligence

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Elements of artificial intelligence - course description

Aim of the course

• Familiarize students with the concept of artificial neural networks and their learning algorithms,
• Familiarize students with the concept of fuzzy sets and fuzzy inference mechanism,
• Familiarize students with different graph search strategies.
• Teach students to solve practical engineering problems using artificial intelligence methods.

Prerequisites

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Principles of programming, Algorithms and data structures

Scope

Artificial neural networks.Biological neuron. Mathematical model of a neuron. Simple
perceptron. Perceptron learning rule. Perceptron limitations. Models of neurons and their properties. Adaline and Madaline architectures. Multilayer neural networks. Learning of single-layer neural network. Learning of multi-layer neural network. Backpropagation algorithm. Models of dynamic neurons. Dynamic neural networks. Sample applications of artificial neural networks.

Fuzzy sets and neuro-fuzzy systems. Fuzzy sets and fuzzy logic. Operations on fuzzy sets. Fuzzy inference. Fuzzy rules. Neuro-fuzzy structures and learning algorithms. Sample applications of fuzzy systems.

Graph search strategies. The breadth-first search algorithm. The depth-first search algorithm. The A* search algorithm. Heuristic functions. Memory and time complexity. The minimax algorithm. The alpha-beta pruning algorithm.

Teaching methods

Lecture, teaching laboratory classes.

Learning outcomes and methods of theirs verification

Outcome description	Outcome symbols	Methods of verification	The class form

Assignment conditions

Lecture - the passing criterion is a sufficient mark from the final test.
Laboratory - the passing criterion are positive marks for laboratory exercises and tests.
Final mark components = lecture: 50% + teaching laboratory: 50%

Recommended reading

1. Russell S., Norvig P.: Artificial Intelligence: A Modern Approach, Prentice Hall, 2009.
2. Bishop C.M., Hinton G. : Neural Networks for Pattern Recognition, Clarendon Press, Oxford, 1995.
3. Edelkamp S., Schroedl S.: Heuristic Search: Theory and Applications, Morgan Kaufmann, 2012. 
4. Zimmermann H-J.: Fuzzy Set Theory and Its Applications, Springer, 2006.

Further reading

1. Bishop C.: Pattern Recognition and Machine Learning, Springer Verlag, 2006.
2. Goodfellow I., Bengio Y., Courville A.: Deep Learning, MIT Press, 2016.
3. Ross. T.: Fuzzy Logic with Engineering Applications, Wiley, 2004.

Notes

Modified by prof. dr hab. inż. Józef Korbicz (last modification: 10-04-2022 11:47)