Course detail

Soft Computing

FIT-SFCAcad. year: 2023/2024

Soft computing covers non-traditional technologies or approaches to solving hard real-world problems. Content of course, in accordance with meaning of its name, is as follow: Tolerance of imprecision and uncertainty as the main attributes of soft computing theories. Neural networks. Fuzzy logic. Nature inspired optimization algorithms. Probabilistic reasoning. Rough sets. Chaos. Hybrid approaches (combinations of neural networks, fuzzy logic and genetic algorithms).

Language of instruction

Czech

Number of ECTS credits

Mode of study

Not applicable.

Guarantor

doc. Ing. Vladimír Janoušek, Ph.D.

Department

Department of Intelligent Systems (UITS)

Entry knowledge

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

Rules for evaluation and completion of the course

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

Aims

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 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 low-cost intelligent machines.

Study aids