Course detail
Artificial Intelligence
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
Entry knowledge
Rules for evaluation and completion of the course
The computer exercises are compulsory, the properly excused missed computer exercises can be compensate.
Aims
Students who completed the course should be able to:
- explain the principles of artificial intelligence in terms of their technological applicability;
- define the paradigms of selected artificial neural networks, i.e., the perceptron, and the
backpropagation and convolutional neural networks;
- discuss and verify the setting of the parameters of a neural network;
- evaluate the usability of artificial neural networks in various target areas;
- characterize the architectures and functionalities of knowledge systems;
- create a knowledge base for the NPSCORE expert system,
- identify and select application fields to host expert systems;
- employ artificial intelligence to process optical information.
Study aids
Prerequisites and corequisites
Basic literature
RUSESELL, Stuart a NORVIG, Peter. Artificial Intelligence. A Modern Aproach. New Jersey: Prentice Hall 2010. 1132 s. ISBN-13: 978-0-13-604259 (EN)
Recommended reading
SONKA, Milan, HLAVAC, Vaclav a BOYLE, Rogert. Image Processing, Analysis and Machine Vision. Toronto: Thomson, 2008. 829 s. ISBN 978-0-495-24438-7. (EN)
Classification of course in study plans
- Programme BPC-AMT Bachelor's 3 year of study, winter semester, compulsory
- Programme BPC-TLI Bachelor's 0 year of study, winter semester, elective
- Programme BPC-SEE Bachelor's 0 year of study, winter semester, elective
- Programme BPC-MET Bachelor's 0 year of study, winter semester, elective
- Programme BPC-IBE Bachelor's 0 year of study, winter semester, elective
- Programme BPC-ECT Bachelor's 0 year of study, winter semester, elective
- Programme BPC-AUD Bachelor's
specialization AUDB-TECH , 0 year of study, winter semester, elective
specialization AUDB-ZVUK , 0 year of study, winter semester, elective
Type of course unit
Lecture
Teacher / Lecturer
Syllabus
1. Organization of teaching, Intelligence
2. Artificial Intelligence – concepts
3. Artificial neural networks - paradigms, Perceptron
4. Multilayer neural network with Backpropagation learning algorithm
5. Kohonen's self-organizing map, Hopfield network, RCE network
6. Kohonen's self-organizing map, Hopfield network, RCE network
7. Expert Systems - representation of knowledge, problem solving
8. Expert Systems - definition, structure, knowledge base, application
9. Principles of computer vision
10. Principles of computer vision
11. Convolutional neural network
12. Convolutional neural network
13. Intelligent systems
Exercise in computer lab
Teacher / Lecturer
Syllabus
1. Úvod + zadání Projektu 1
2. Práce doma - Projekt 1
3. Základy Matlabu
4. Umělé neuronové sítě
5. Umělé neuronové sítě
6. Umělé neuronové sítě
7. Projekt 1 - obhajoba
8. Expertní systémy + zadání Projektu 2
9. Projekt 1 - obhajoba
10. Počítačové vidění
11. Umělé neuronové sítě
12. Projekt 2 - obhajoba
13. Projekt 2 - obhajoba