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Course detail
FEKT-BPC-UINAcad. year: 2023/2024
The course discusses the basic methods, subdomains, and activities associated with artificial intelligence, including the training and application of artificial neural networks, knowledge base formation, the structure and functioning of knowledge systems, and optical information processing.
Language of instruction
Number of ECTS credits
Mode of study
Guarantor
Department
Entry knowledge
Rules for evaluation and completion of the course
Aims
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Prerequisites and corequisites
Basic literature
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Classification of course in study plans
specialization AUDB-TECH , 0 year of study, winter semester, electivespecialization AUDB-ZVUK , 0 year of study, winter semester, elective
Lecture
Teacher / Lecturer
Syllabus
1. Organization of teaching, Intelligence2. Artificial Intelligence – concepts3. Artificial neural networks - paradigms, Perceptron4. Multilayer neural network with Backpropagation learning algorithm5. Kohonen's self-organizing map, Hopfield network, RCE network6. Kohonen's self-organizing map, Hopfield network, RCE network7. Expert Systems - representation of knowledge, problem solving8. Expert Systems - definition, structure, knowledge base, application9. Principles of computer vision10. Principles of computer vision11. Convolutional neural network12. Convolutional neural network13. Intelligent systems
Exercise in computer lab
1. Úvod + zadání Projektu 12. Práce doma - Projekt 13. Základy Matlabu 4. Umělé neuronové sítě 5. Umělé neuronové sítě 6. Umělé neuronové sítě 7. Projekt 1 - obhajoba8. Expertní systémy + zadání Projektu 29. Projekt 1 - obhajoba10. Počítačové vidění 11. Umělé neuronové sítě 12. Projekt 2 - obhajoba13. Projekt 2 - obhajoba