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Course detail
FEKT-MPC-UINAcad. year: 2023/2024
The course discusses the basic methods and subdomains of artificial intelligence, namely, machine learning, the structure and activity of knowledge systems, optical information processing, and approaches to the training and application of artificial neural networks.
Language of instruction
Number of ECTS credits
Mode of study
Guarantor
Department
Entry knowledge
Rules for evaluation and completion of the course
Aims
Study aids
Prerequisites and corequisites
Basic literature
Recommended reading
Classification of course in study plans
specialization AUDM-TECH , 1 year of study, winter semester, compulsory-optionalspecialization AUDM-ZVUK , 1 year of study, winter semester, compulsory-optional
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