1. SylabUZ
2. Faculty of Computer Science, Electrical Engineering and Automatics
3. winter term 2021/2022
4. Computer Science - Second-cycle Erasmus programme
5. Artificial intelligence techniques

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Artificial intelligence techniques - course description

Aim of the course

• Familiarize students with the selected AI techniques, current trends and application areas of AI systems.
• Teach students how to design, develop and train artificial intelligence models and how to interpret and assess their results.

Prerequisites

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Scope

Introduction to artificial intelligence. Social and biological inspirations. General assumptions. Learning and organization of data. Comparison of artificial intelligence techniques to analytical methods.

Feed forward neural networks. Structures and their properties. Backpropagation. Examples of neural network applications in image recognition.

Deep learning. Convolutional neural networks. Autoencoders, Long Short Term Memory.

Fuzzy and neuro-fuzzy systems. Fuzzy sets and fuzzy logic. Operations on fuzzy sets. Fuzzy inference. Fuzzy rules. Neuro-fuzzy systems. Gradient descent based learning algorithm for neuro-fuzzy systems. 

Evolutionary algorithms and swarm intelligence. Basic concepts. General schema of the evolutionary algorithm. Evolutionary strategies. Encoding methods. Evolutionary operators. Simple genetic algorithm.  Crossing operators. Swarm intelligence algorithms.

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. Goodfellow I., Bengio Y., Courville A.: Deep Learning, MIT Press, 2016.
2. Russell S., Norvig P.: Artificial Intelligence: A Modern Approach, Pearson, 2020.
3. Bishop C.M., Hinton G.: Neural Networks for Pattern Recognition, Clarendon Press, Oxford, 1995.
4. Zimmermann H-J.: Fuzzy Set Theory and Its Applications, Springer, 2006.
5. Rutkowska D.:Neuro-Fuzzy Architectures and Hybrid Learning, Springer, 2001.
6. Goldberg D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, 1989.
7. Dasgupta D., Michalewicz Z.: Evolutionary Algorithms in Engineering Applications, Springer-Verlag, 2010
8. Eberhart R.C., Shi Y., Kennedy J.: Swarm Intelligence, Morgan Kaufmann, 2001.
9. Haykin S.: Neural Networks: A Comprehensive Foundation (2nd Edition), Prentice Hall, 1998.

Further reading

 

1. Murphy K.P.: Machine Learning. A Probabilistic Perspective, MIT Press, 2013.
2. Theodoridis S.: Machine Learning. A Bayesian and Optimization Perspective. Academic Press, 2015. 

Notes

Modified by dr hab. inż. Marek Kowal, prof. UZ (last modification: 20-07-2021 10:08)