Course: Computing Intelligence

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Course title Computing Intelligence
Course code UAI/766
Organizational form of instruction Lecture
Level of course Bachelor
Year of study not specified
Frequency of the course In each academic year, in the summer semester.
Semester Summer
Number of ECTS credits 3
Language of instruction Czech
Status of course Optional
Form of instruction Face-to-face
Work placements This is not an internship
Recommended optional programme components None
Lecturer(s)
  • Skrbek Miroslav, Ing. Ph.D.
Course content
1/ Introduction to computing intelligence 2/ Neural networks, taxonomy, topology 3/ Associative neural networks 4/ Feed-forward networks, supervised learning, Perceptron, RBF, Backpropagation algorithm 5/ Self organising neural networks, SOM 6/ Recurrent neural networks, TDNN networks 7/ Pulse networks, models, learning principles 8/ Reinforcement learning 9/ Application of neural networks 10/ Fuzzy systems: fuzzy set, fuzzy rules, fuzzyfication, inference, defuzzyfication. 11/ Genetic algorithms, principle, differential evolution, evolutionary programming 12/ Optimization inspired by nature, PSO (Particle Swarm Optimization) algorithm and ANT (Ant Colony Optimization) 13/ Artificial immune systems

Learning activities and teaching methods
Monologic (reading, lecture, briefing)
  • Class attendance - 26 hours per semester
  • Preparation for exam - 40 hours per semester
  • Semestral paper - 20 hours per semester
Learning outcomes
This course is focused on computation intelligence algorithms especially neural networks. Students learn basic types of neural networks, fuzzy systems, evolutionary algorithms and nature inspired optimization algorithms.
In this course students acquire basic knowledge of the neural networks, fuzzy logic and nature inspired optimization algorithms.
Prerequisites
Basic knowledge of mathematics and differential calculus.

Assessment methods and criteria
Written examination, Seminar work

Each student may take 100 points (70 points examination, 30 points project). For passing examination, the total number of points (examination and tutorial) must be greater or equal to 50 and the examination test must be evaluated to one half points or more and the project must be evaluated to 15 points or more. If any of these conditions is not satisfied, the student fails.
Recommended literature
  • Andries P. Engelbrecht: Computational intelligence: An Introduction. Wiley; 2 edition, 2007. ISBN: 978-0470035610.
  • Simon Haykin: Neural Networks and Learning Machines. Third Edition. Prentice Hill. 2009. ISBN 978-0-13-147139-9..
  • ŠÍMA, J. - NERUDA, R.: Teoretické otázky neuronových sítí. Matfyzpress, Praha, 1996..


Study plans that include the course
Faculty Study plan (Version) Category of Branch/Specialization Recommended year of study Recommended semester
Faculty: Faculty of Science Study plan (Version): Mathematics for future teachers (1) Category: Mathematics courses - Recommended year of study:-, Recommended semester: Summer
Faculty: Faculty of Science Study plan (Version): Secondary Schools Teacher Training in Mathematics (1) Category: Pedagogy, teacher training and social care - Recommended year of study:-, Recommended semester: Summer
Faculty: Faculty of Science Study plan (Version): Secondary Schools Teacher Training in Mathematics (1) Category: Pedagogy, teacher training and social care - Recommended year of study:-, Recommended semester: Summer
Faculty: Faculty of Science Study plan (Version): Applied Mathematics (2010) Category: Mathematics courses 3 Recommended year of study:3, Recommended semester: Summer
Faculty: Faculty of Science Study plan (Version): Applied Informatics (1) Category: Informatics courses - Recommended year of study:-, Recommended semester: Summer
Faculty: Faculty of Science Study plan (Version): Secondary Schools Teacher Training in Mathematics (1) Category: Pedagogy, teacher training and social care - Recommended year of study:-, Recommended semester: Summer
Faculty: Faculty of Science Study plan (Version): Applied Informatics (1) Category: Informatics courses - Recommended year of study:-, Recommended semester: Summer
Faculty: Faculty of Science Study plan (Version): Mathematics for future teachers (1) Category: Mathematics courses - Recommended year of study:-, Recommended semester: Summer
Faculty: Faculty of Science Study plan (Version): Applied Informatics (1) Category: Informatics courses - Recommended year of study:-, Recommended semester: Summer
Faculty: Faculty of Science Study plan (Version): Secondary Schools Teacher Training in Mathematics (2012) Category: Pedagogy, teacher training and social care - Recommended year of study:-, Recommended semester: Summer
Faculty: Faculty of Science Study plan (Version): Applied Informatics (1) Category: Informatics courses - Recommended year of study:-, Recommended semester: Summer
Faculty: Faculty of Science Study plan (Version): Applied Informatics (1) Category: Informatics courses - Recommended year of study:-, Recommended semester: Summer
Faculty: Faculty of Science Study plan (Version): Applied Informatics (1) Category: Informatics courses - Recommended year of study:-, Recommended semester: Summer
Faculty: Faculty of Science Study plan (Version): Mathematics for future teachers (1) Category: Mathematics courses - Recommended year of study:-, Recommended semester: Summer
Faculty: Faculty of Science Study plan (Version): Mathematics for future teachers (1) Category: Mathematics courses - Recommended year of study:-, Recommended semester: Summer