1. SylabUZ
2. Wydział Nauk Inżynieryjno-Technicznych
3. semestr zimowy 2021/2022
4. Informatyka - Program Erasmus pierwszego stopnia
5. Elements of artificial intelligence

Wygeneruj PDF dla tej strony

Elements of artificial intelligence - opis przedmiotu

Cel przedmiotu

• 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.

Wymagania wstępne

Zapisz zmiany

Principles of programming, Algorithms and data structures

Zakres tematyczny

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.

Metody kształcenia

Lecture, teaching laboratory classes.

Efekty uczenia się i metody weryfikacji osiągania efektów uczenia się

Opis efektu	Symbole efektów	Metody weryfikacji	Forma zajęć

Warunki zaliczenia

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%

Literatura podstawowa

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.

Literatura uzupełniająca

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.

Uwagi

Zmodyfikowane przez prof. dr hab. inż. Józef Korbicz (ostatnia modyfikacja: 30-08-2021 14:32)