Course detail

Soft Computing

FIT-SFCAcad. year: 2020/2021

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).

Supervisor

doc. Ing. František Zbořil, CSc.

Department

Department of Intelligent Systems (UITS)

Learning outcomes of the course unit

  • 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.

Prerequisites

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

Co-requisites

Not applicable.

Recommended optional programme components

Not applicable.

Recommended or required reading

Graube, D.: Principles of Artificial Neural networks, World Scientific Publishing Co. Pte. Ltd., third edition, 2013
Kriesel, D.: A Brief Introduction to Neural Networks, 2005, http://www.dkriesel.com/en/science/neural_networks
Kruse, R., Borgelt, Ch., Braune, Ch., Mostaghim, S., Steinbrecher, M.: Computational Intelligence, Springer, second edition 2016, ISBN 978-1-4471-7296-3
Munakata, T.: 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
Russell,S., Norvig,P.: Artificial Intelligence, Prentice-Hall, Inc., third edition 2010, ISBN 0-13-604259-7
Russell,S., Norvig,P.: Artificial Intelligence, Prentice-Hall, Inc., 1995, ISBN 0-13-360124-2, third edition 2010, ISBN 0-13-604259-7
Shi, Z.: Advanced Artificial Intelligence, World Scientific Publishing Co. Pte. Ltd., 2011, ISBN-13 978-981-4291-34-7

Planned learning activities and teaching methods

Not applicable.

Assesment methods and criteria linked to learning outcomes

  • Mid-term written examination - 15 points.
  • Project - 30 points.
  • Final written examination - 55 points; The minimal number of points necessary for successful clasification is 25 (otherwise, no points will be assigned).

Exam prerequisites:
At least 20 points earned during semester (mid-term test and project).

Language of instruction

Czech

Work placements

Not applicable.

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.

Classification of course in study plans

  • Programme IT-MGR-2 Master's

    branch MPV , any year of study, winter semester, 5 credits, compulsory-optional
    branch MGM , any year of study, winter semester, 5 credits, elective
    branch MSK , any year of study, winter semester, 5 credits, elective
    branch MIS , any year of study, winter semester, 5 credits, elective
    branch MBS , any year of study, winter semester, 5 credits, elective
    branch MMI , any year of study, winter semester, 5 credits, elective
    branch MMM , any year of study, winter semester, 5 credits, compulsory-optional

  • Programme MITAI Master's

    specialization NADE , any year of study, winter semester, 5 credits, elective
    specialization NBIO , any year of study, winter semester, 5 credits, elective
    specialization NGRI , any year of study, winter semester, 5 credits, elective
    specialization NNET , any year of study, winter semester, 5 credits, elective
    specialization NVIZ , any year of study, winter semester, 5 credits, elective
    specialization NCPS , any year of study, winter semester, 5 credits, elective
    specialization NSEC , any year of study, winter semester, 5 credits, elective
    specialization NEMB , any year of study, winter semester, 5 credits, elective
    specialization NHPC , any year of study, winter semester, 5 credits, elective
    specialization NISD , any year of study, winter semester, 5 credits, elective
    specialization NIDE , any year of study, winter semester, 5 credits, compulsory
    specialization NMAL , any year of study, winter semester, 5 credits, compulsory
    specialization NMAT , any year of study, winter semester, 5 credits, elective
    specialization NSEN , any year of study, winter semester, 5 credits, elective
    specialization NVER , any year of study, winter semester, 5 credits, elective
    specialization NSPE , any year of study, winter semester, 5 credits, elective

  • Programme IT-MGR-2 Master's

    branch MIN , 1. year of study, winter semester, 5 credits, compulsory

  • Programme MITAI Master's

    specialization NISY , 1. year of study, winter semester, 5 credits, compulsory

  • Programme IT-MGR-2 Master's

    branch MBI , 2. year of study, winter semester, 5 credits, compulsory

Type of course unit

 

Lecture

26 hours, optionally

Teacher / Lecturer

doc. Ing. František Zbořil, CSc.

Syllabus


  1. Introduction. Biological and artificial neuron, artificial neural networks.
  2. Acyclic and feedforward neural networks, backpropagation algorithm. 
  3. Neural networks with RBF neurons. Competitive networks.

  4. Neocognitron and convolutional neural networks.
  5. Recurrent neural networks (Hopfield networks, Boltzmann machine).
  6. Recurrent neural networks (LSTM, GRU).
  7. Genetic algorithms.
  8. Optimization algorithms inspired by nature.
  9. Fuzzy sets and fuzzy logic.
  10. Probabilistic reasoning, Bayesian networks.
  11. Rough sets.
  12. Chaos.
  13. Hybrid approaches (neural networks, fuzzy logic, genetic algorithms).

Project

26 hours, compulsory

Teacher / Lecturer

doc. Ing. František Zbořil, CSc.

Syllabus

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