Neural Networks and Computational Intelligence
Code | Completion | Credits | Range |
---|---|---|---|
PI-NSV | ZK | 4 | 3C |
- Garant předmětu:
- Pavel Surynek
- Lecturer:
- Pavel Surynek
- Tutor:
- Pavel Surynek
- 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 nature-inspired 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. 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
- 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:
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[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.
- Note:
- Further information:
- https://courses.fit.cvut.cz
- Time-table for winter semester 2024/2025:
- Time-table is not available yet
- Time-table for summer semester 2024/2025:
- Time-table is not available yet
- The course is a part of the following study plans:
-
- Informatics (doctoral) (compulsory elective course)
- Informatics (compulsory elective course)