Neural Networks and Computational Intelligence
Code  Completion  Credits  Range 

PINSV  ZK  4  0+3 
 Lecturer:
 Jiří Bíla (guarantor), Miroslav Skrbek
 Tutor:
 Pavel Surynek (guarantor), Miroslav Skrbek
 Supervisor:
 Department of Applied Mathematics
 Synopsis:

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 natureinspired optimization. Principles of machine learning, deep neural networks and deep learning.
 Requirements:
 Syllabus of lectures:

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. Selforganization for analyzing and extracting knowledge from data.
8. Inductive modeling methods, automated design of the model by computational intelligence methods.
9. Natureinspired optimization techniques.
10. Machine learning using neural networks
11. Deep neural networks and deep learning
 Syllabus of tutorials:
 Study Objective:

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.
 Study materials:

[1] Simon Haykin: Neural Networks and Learning Machines. Third Edition. Prentice Hall, 2009, ISBN 9780131471399.
[2] Sundararajan, N., Saratchandran, P.: Parallel Architectures for Artificial Neural Networks, IEEE Computer Society Press, 1998, ISBN 0818683996.
[3] Šíma, J., Neruda, R.: Theoretical Issues of Neural Networks
MATFYZPRESS, Prague, 1996, ISBN 8085863189.
[4] Aggarwal, Charu C.: Neural Networks and Deep Learning, Springer 2018, ISBN 9783319944630.
 Note:
 Further information:
 https://courses.fit.cvut.cz/PINSV/
 Timetable for winter semester 2018/2019:
 Timetable is not available yet
 Timetable for summer semester 2018/2019:
 Timetable is not available yet
 The course is a part of the following study plans:

 Informatics (VO)