Course objectives:
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The goal of the course is to familiarise students with basic types of artificial neural networks and their learning, and with the fundamentals of evolutionary techniques.
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Requirements on student
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Credit: solution of a given task from the field of neural networks and evolutionary techniques
Exam: knowledge corresponding to the extent of the course (lectures + seminars)
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Content
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1. Biological ground of neural networks, models of neuron.
2. Models of neural networks, neural networks learning principles.
3. Multilayer perceptron.
4. Algorithm backpropagation.
5. Hopfield network.
6. Kohonen network.
7. Simulated annealing.
8.-9. Genetic algorithms.
10. Evolutionary strategies.
11. Evolutionary algorithms.
12.-13. Demonstration of practical usage of neural networks and evolutionary techniques.
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Activities
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Fields of study
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Studentům je po začátku příslušného semestru k dispozici kurz v Google Classroom se všemi podstatnými studijními podklady.
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Guarantors and lecturers
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Literature
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Recommended:
Umělá inteligence.
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Recommended:
Mařík, Vladimír. Umělá inteligence (1).
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Recommended:
Mařík, Vladimír a kol. Umělá inteligence (2). Academia, Praha, 1997.
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Recommended:
Mařík, Vladimír a kol. Umělá inteligence (3). Academia, Praha, 2001.
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Recommended:
Mařík, Vladimír a kol. Umělá inteligence (4). Academia, Praha, 2003.
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On-line library catalogues
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Time requirements
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All forms of study
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Activities
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Time requirements for activity [h]
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Preparation for an examination (30-60)
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51
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Undergraduate study programme term essay (20-40)
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40
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Practical training (number of hours)
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26
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Contact hours
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39
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Total
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156
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Prerequisites
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Knowledge - students are expected to possess the following knowledge before the course commences to finish it successfully: |
využít znalosti z matematické analýzy a lineární algebry |
Skills - students are expected to possess the following skills before the course commences to finish it successfully: |
aplikovat znalosti z matematické analýzy a lineární algebry |
Competences - students are expected to possess the following competences before the course commences to finish it successfully: |
N/A |
N/A |
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Learning outcomes
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Knowledge - knowledge resulting from the course: |
vysvětlit činnost základních typů umělých neuronových sítí |
vysvětlit princip evoluční technik a genetických algoritmů |
vysvětlit výsledky dosažené umělými neuronovými sítěmi a genetickými algoritmy v konkrétních úlohách |
zdůvodnit vhodnost použití konkrétní umělé neuronové sítě pro řešení konkrétní praktické úlohy |
Skills - skills resulting from the course: |
analyzovat činnost základních typů umělých neuronových sítí |
aplikovat evoluční techniky a genetické algoritmy při řešení reálných úloh |
aplikovat vhodný typ umělé neuronové sítě pro konkrétní praktickou úlohu |
vyhodnotit a analyzovat výsledky dosažené umělými neuronovými sítěmi a genetickými algoritmy v konkrétních úlohách |
Competences - competences resulting from the course: |
N/A |
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Assessment methods
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Knowledge - knowledge achieved by taking this course are verified by the following means: |
Combined exam |
Seminar work |
Skills - skills achieved by taking this course are verified by the following means: |
Combined exam |
Seminar work |
Skills demonstration during practicum |
Competences - competence achieved by taking this course are verified by the following means: |
Combined exam |
Seminar work |
Skills demonstration during practicum |
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Teaching methods
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Knowledge - the following training methods are used to achieve the required knowledge: |
Lecture |
Lecture with visual aids |
Practicum |
Self-study of literature |
Individual study |
Skills - the following training methods are used to achieve the required skills: |
Lecture |
Lecture with visual aids |
Practicum |
Task-based study method |
Competences - the following training methods are used to achieve the required competences: |
Individual study |
Lecture |
Lecture with visual aids |
Practicum |
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