Course details

Soft Computing

SFC Acad. year 2024/2025 Winter semester 5 credits

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).

Guarantor

Course coordinator

Language of instruction

Czech

Completion

Credit+Examination (written)

Time span

  • 26 hrs lectures
  • 26 hrs projects

Assessment points

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

Department

Lecturer

Instructor

Learning objectives

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.

Prerequisite knowledge and skills

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

Study literature

  • 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

Fundamental literature

Study supports

Syllabus of lectures

  1. Introduction. Neural networks. Backpropagation.

  2. Networks with RBF. 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. Nature-inspired optimization algorithms.

  9. Probabilistic inference, Bayesian networks.

  10. Rough sets and their applications.

  11. Fundamentals of chaos theory.

  12. Hybrid approaches.

  13. Summary, conclusion.

Syllabus - others, projects and individual work of students

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

Progress assessment

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

Schedule

DayTypeWeeksRoomStartEndCapacityLect.grpGroupsInfo
Tue lecture 1., 2., 3., 4., 5., 6., 7., 8., 9., 10. of lectures G202 12:0014:5080 1MIT 2MIT NIDE NISY - NIDE NMAL xx Janoušek
Tue lecture 11., 12., 13. of lectures G202 12:0014:5080 1MIT 2MIT NIDE NISY - NIDE NMAL xx

Course inclusion in study plans

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