| 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 | Compulsory-optional |
| Form of instruction | Face-to-face |
| Work placements | This is not an internship |
| Recommended optional programme components | None |
| Lecturer(s) |
|---|
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| Course content |
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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
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| Learning activities and teaching methods |
Monologic (reading, lecture, briefing)
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| Learning outcomes |
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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 |
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Basic knowledge of mathematics and differential calculus.
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| Assessment methods and criteria |
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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 |
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| Study plans that include the course |
| Faculty | Study plan (Version) | Category of Branch/Specialization | Recommended semester | |
|---|---|---|---|---|
| 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 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): 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): Mathematics for future teachers (1) | Category: Mathematics courses | - | Recommended year of study:-, Recommended semester: Summer |