Subject description - PI-NSV

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PI-NSV Neural Networks and Computational Intelligence
Roles:  Extent of teaching:3C
Department:18105 Language of teaching:
Guarantors:Surynek P. Completion:ZK
Lecturers:Surynek P. Credits:4
Tutors:Surynek P. Semester:L

Web page:

https://courses.fit.cvut.cz

Anotation:

Theoretical foundations of neural networks with a focus on advanced paradigms and the use of neural networks as a model for data analysis and data mining. Networks with dynamically generated topology during learning developed on the principles of inductive modeling. Evolutionary techniques and nature-inspired optimization. Principles of machine learning, deep neural networks and deep learning.

Study targets:

To familiarize students with theoretical backgrounds and advanced methods in the field of neural networks, especially in the field of learning, development of topology and modeling of data analysis and extraction.

Course outlines:

1. Theoretical foundations of artificial neural networks.
2. Neural networks for classification and approximation.
3. Methods of learning (with and without a supervisor), advanced gradient methods and evolutionary learning algorithms.
4. Development of neural network topology by evolutionary techniques, genetic programming.
5. Networks with complex scales.
6. Self-organization for analyzing and extracting knowledge from data.
8. Inductive modeling methods, automated design of the model by computational intelligence methods.
9. Nature-inspired optimization techniques.
10. Machine learning using neural networks
11. Deep neural networks and deep learning

Exercises outline:

Literature:

[1] Simon Haykin: Neural Networks and Learning Machines. Third Edition. Prentice Hall, 2009, ISBN 978-0-13-147139-9.
[2] Sundararajan, N., Saratchandran, P.: Parallel Architectures for Artificial Neural Networks, IEEE Computer Society Press, 1998, ISBN 0-8186-8399-6.
[3] Šíma, J., Neruda, R.: Theoretical Issues of Neural Networks
MATFYZPRESS, Prague, 1996, ISBN 80-85863-18-9.
[4] Aggarwal, Charu C.: Neural Networks and Deep Learning, Springer 2018, ISBN 978-3-319-94463-0.

Requirements:

Subject is included into these academic programs:

Program Branch Role Recommended semester

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Responsible person: prof. Ing. Jiří Jakovenko, Ph.D.