Soft Computing

• 26 hrs lectures
• 26 hrs projects

• 55 pts final exam (written part)
• 15 pts mid-term test (written part)
• 30 pts projects

To give students knowledge of soft-computing theories fundamentals, i.e. of fundamentals of non-traditional technologies and approaches to solving hard real-world problems.

• Students will acquaint with basic types of neural networks and with their applications.
• Students will acquaint with fundamentals of theory of fuzzy sets and fuzzy logic including design of fuzzy controller.
• Students will acquaint with theory and applications of reinforcememnt lerning.
• Students will acquaint with nature-inspired optimization algorithms.
• Students will acquaint with fundamentals of probability reasoning theory.
• Students will acquaint with fundamentals of rouhg sets theory and with use of these sets for data mining.
• Students will acquaint with fundamentals of chaos theory.
• Students will learn terminology in Soft-computing field both in Czech and in English languages.
• Students awake the importance of tolerance of imprecision and uncertainty for design of robust and intelligent machines and systems.

• Programming in C++ or Python languages.
• Basic knowledge of differential calculus and probability theory.

• Graube, D.: Principles of Artificial Neural networks, World Scientific Publishing Co. Pte. Ltd., third edition, 2013
• Rutkowski, L.: Flexible Neuro-Fuzzy Systems, Kluwer Academic Publishers, 2004, ISBN 1-4020-8042-5
• Shi, Z.: Advanced Artificial Intelligence, World Scientific Publishing Co. Pte. Ltd., 2011, ISBN-13 978-981-4291-34-7

• Kriesel, D.: A Brief Introduction to Neural Networks, 2005, Fundamentals of the New Artificial Intelligence, Springer-Verlag New York, Inc., 2008. ISBN 978-1-84628-838-8 
• Rutkowski, L.: Flexible Neuro-Fuzzy Systems, Kluwer Academic Publishers, 2004, ISBN 1-4020-8042-5
• Russel,S., Norvig,P.: Artificial Intelligence, Prentice-Hall, Inc., third edition 2010, ISBN 0-13-604259-7

• Informace pro studenty

1. Introduction. Neural networks. Backpropagation.

2. RBF networks. Hopfield-type recurrent networks. Boltzmann machine.

3. Convolutional neural networks. Deep learning.

4. Time series. LSTM, GRU recurrent networks.

5. Fuzzy sets, fuzzy logic and their applications.

6. Markov decision process and reinforcement learning.

7. Genetic algorithms and genetic programming.

8. ACO, PSO and other nature-inspired optimization algorithms.

9. Probabilistic inference, Bayesian networks.

10. Rough sets and their applications.

11. Fundamentals of chaos theory.

12. Hybrid approaches.

13. Liquid neural nets.

Individual project - solving real-world problem (classification, optimization, association, controlling).

• Mid-term written examination - 15 points.
• Project - 30 points.
• Final written examination - 55 points, miinimum 25.