Course detail

Soft Computing

FIT-SFCAcad. year: 2024/2025

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. Reinforcement learning. 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

5

Mode of study

Not applicable.

Guarantor

doc. Ing. Vladimír Janoušek, Ph.D.

Department

Department of Intelligent Systems (UITS)

Entry knowledge

  • Programming in C++ or Python 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 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.

Study aids

Prerequisites and corequisites

Not applicable.

Basic literature

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 
Russel,S., Norvig,P.: Artificial Intelligence, Prentice-Hall, Inc., third edition 2010, ISBN 0-13-604259-7
Rutkowski, L.: Flexible Neuro-Fuzzy Systems, Kluwer Academic Publishers, 2004, ISBN 1-4020-8042-5

Recommended reading

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

Elearning

eLearning: currently opened course

Classification of course in study plans

  • Programme MITAI Master's

    specialization NGRI , 0 year of study, winter semester, elective
    specialization NADE , 0 year of study, winter semester, elective
    specialization NISD , 0 year of study, winter semester, elective
    specialization NMAT , 0 year of study, winter semester, elective
    specialization NSEC , 0 year of study, winter semester, elective
    specialization NISY up to 2020/21 , 1 year of study, winter semester, compulsory
    specialization NNET , 0 year of study, winter semester, elective
    specialization NMAL , 0 year of study, winter semester, compulsory
    specialization NCPS , 0 year of study, winter semester, elective
    specialization NHPC , 0 year of study, winter semester, elective
    specialization NVER , 0 year of study, winter semester, elective
    specialization NIDE , 0 year of study, winter semester, compulsory
    specialization NISY , 1 year of study, winter semester, compulsory
    specialization NEMB , 0 year of study, winter semester, elective
    specialization NSPE , 0 year of study, winter semester, elective
    specialization NEMB , 0 year of study, winter semester, elective
    specialization NBIO , 0 year of study, winter semester, elective
    specialization NSEN , 0 year of study, winter semester, elective
    specialization NVIZ , 0 year of study, winter semester, elective

Type of course unit

 

Lecture

26 hod., optionally

Teacher / Lecturer

doc. Ing. Vladimír Janoušek, Ph.D.

Syllabus

  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.

Project

26 hod., compulsory

Teacher / Lecturer

doc. Ing. Vladimír Janoušek, Ph.D.
Ing. Petr Veigend, Ph.D.
doc. Ing. František Zbořil, CSc.

Syllabus

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

Elearning

eLearning: currently opened course