Course: Soft Computing

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Course title Soft Computing
Course code KDMML/XBVIN
Organizational form of instruction Lecture + Lesson
Level of course Master
Year of study not specified
Semester Summer
Number of ECTS credits 4
Language of instruction Czech
Status of course Compulsory-optional
Form of instruction Face-to-face
Work placements This is not an internship
Recommended optional programme components None
Lecturer(s)
  • Křupka Jiří, doc. Ing. PhD.
Course content
1. Ambient intelligence and social impacts. 2. Introduction to artificial intelligence and soft computing. 3. Industry 4.0 and examples of the use of intelligent systems. 4. Fuzzy logic. 5. Fuzzy inference systems. 6. Example of Mamdani fuzzy inference system design. 7. Artificial Neural networks. 8. Example of artificial neural network design with supervised learning. 9. Neuro-fuzzy systems. 10. Classification models. 11. Prediction models. 12. Evolutionary algorithms.

Learning activities and teaching methods
Monologic (reading, lecture, briefing), Dialogic (discussion, interview, brainstorming), Laboratory work
  • Contact teaching - 13 hours per semester
  • Practical training - 26 hours per semester
  • Home preparation for classes - 26 hours per semester
  • Preparation for a credit (assessment) - 13 hours per semester
  • Preparation for an exam - 32 hours per semester
  • Preparation of a presentation (report) - 10 hours per semester
Learning outcomes
The course aim is to introduce students with the issues of Soft Computing (Computational Intelligence). The course is focused on acquiring knowledge, mastering algorithms, mastering tools and developing skills in the design and implementation of systems based on computational intelligence methods so that they can work with uncertainty in data for more effective management and decision-making.

Prerequisites
unspecified

Assessment methods and criteria
Oral examination, Written examination, Student performance assessment, Work-related product analysis

Recommended literature
  • Křupka, J., Kašparová, M. Úvod do teorie systémů - multimediální opora na CD-ROM. Pardubice, 2007. ISBN 978-80-7194-955-8.
  • Křupka, Jiří. Modelování v kostce pro Matlab a Simulink : distanční opora. Pardubice: Univerzita Pardubice, 2009. ISBN 978-80-7395-162-7.
  • KUNCHEVA, L. I. Fuzzy Classifier Design. A Springer Verlag Company: Germany, 2000. ISBN 80-903024-9.
  • Olej, Vladimír. Úvod do umělé inteligence : moderní přístupy : distanční opora. Pardubice: Univerzita Pardubice, 2010. ISBN 978-80-7395-307-2.
  • Pokorný, Miroslav. Umělá inteligence v modelování a řízení. Praha: BEN - technická literatura, 1996. ISBN 80-901984-4-9.
  • Russell, Stuart J. Artificial intelligence : a modern approach. Harlow: Pearson Education, 2014. ISBN 978-1-292-02420-2.


Study plans that include the course
Faculty Study plan (Version) Category of Branch/Specialization Recommended year of study Recommended semester