Course detail
Artificial Intelligence
FEKT-MUINAcad. year: 2019/2020
The aim of the course is to deepen knowledges and application of artificial intelligence methods. Artificial intelligence – definition, trends. Artificial neural networks, neural networks paradigms, method of backpropagation learning, Kohonen self-organizing maps, Hopfield network, RCE neural network. Knowledge-based systems, knowledge representation, problem solving, structure and activities of expert systems. Optical information processing resources of artificial inteligence. Intelligent robot.
Language of instruction
Number of ECTS credits
Mode of study
Guarantor
Learning outcomes of the course unit
- explain the concept of artificial intelligence from the perspective of its application in technical equipment,
- explain the paradigm for artificial neural network: perceptron, multilayer neural network backpropagation learning, Kohonen self-organizing maps, Hopfield network, RCE neural network,
- discuss and verify the settings of individual parameters of the selected neural network,
- assess the scope of application of artificial neural network,
- explain the architecture and functionality of knowledge systéme,
- create a base of knowledge for expert system NPS32,
- choose the field of application of expert systéme,
- optical information processing devices applied artificial inteligence.
Prerequisites
Co-requisites
Planned learning activities and teaching methods
Assesment methods and criteria linked to learning outcomes
Course curriculum
2. Neuroscience - biological information system, neuron, brain, intelligence
3. Artificial neural networks - definitions, paradigms, Perceptron, learning
4. Multilayer neural network with Backpropagation learning algorithm
5. Kohonen's self-organizing map, Hopfield network, RCE network
6. public holiday 2019
7. Principles of computer vision
8. Principles of computer vision
9. Expert Systems - representation of knowledge, problem solving
10. Expert Systems - definition, structure, knowledge base, application
11. Convolutional neural network
12. Convolutional neural network
13. Intelligent systems
Work placements
Aims
Specification of controlled education, way of implementation and compensation for absences
Recommended optional programme components
Prerequisites and corequisites
Basic literature
Kasabov,N.K.: Foundations of Neural Networks, Fuzzy systems and Knowledge Engineering.The MIT Press,1996,ISBN 0-262-11212-4 (ET)
Mařík V.-Štěpánková O.-Lažanský J.:Umělá inteligence 1. ACADEMIA 1993,Praha,ISBN 80-200-0496-3 (CS)
Mařík V.-Štěpánková O.-Lažanský J.:Umělá inteligence 2. ACADEMIA 1997,Praha,ISBN 80-200-0504-8 (CS)
Russell S.-Norvig P.: Artificial Intelligence a Modern Approach. Prentice Hall 2010, ISBN 978-0-13-604259-4 (CS)
Sonka M., Hlavac V., Boyle R.: Image Processing, Analysis and Machine Vision. Thomson, 2008, ISBN 978-0-495-08252-1 (EN)
Šíma J., Neruda R.: Teoretické otázky neuronových sítí. Matfyzpress, Praha 1996 (CS)
Recommended reading
Mařík V.-Štěpánková O.-Lažanský J.:Umělá inteligence 4. ACADEMIA 2003,Praha,ISBN 80-200-1044-0 (EN)
Schalkoff,R.J.:Artificial Neural Networks. The MIT Press,1997,ISBN 0-07-115554-6 (EN)
Classification of course in study plans
- Programme IBEP-V Master's
branch V-IBP , 2 year of study, winter semester, elective specialised
branch V-IBP , 1 year of study, winter semester, elective specialised - Programme EEKR-M Master's
branch M-EEN , 2 year of study, winter semester, elective interdisciplinary
branch M-KAM , 2 year of study, winter semester, compulsory
branch M-TIT , 1 year of study, winter semester, elective interdisciplinary - Programme EEKR-CZV lifelong learning
branch EE-FLE , 1 year of study, winter semester, compulsory
Type of course unit
Lecture
Teacher / Lecturer
Syllabus
2. Neuroscience - biological information system, neuron, brain, intelligence
3. Artificial neural networks - definitions, paradigms, Perceptron, learning
4. Multilayer neural network with Backpropagation learning algorithm
5. Kohonen's self-organizing map, Hopfield network, RCE network
6. public holiday 2019
7. Principles of computer vision
8. Principles of computer vision
9. Expert Systems - representation of knowledge, problem solving
10. Expert Systems - definition, structure, knowledge base, application
11. Convolutional neural network
12. Convolutional neural network
13. Intelligent systems
Exercise in computer lab
Teacher / Lecturer
Syllabus
2. Basics of Matlab and Computer Vision
3. Perceptron
4. Single-layer neural network
5. Back propagation
6. Project (work at home)
7. Artificial neural networks
8. Computer vision - Convolution
9. Expert systems
10. Project 1 - Report
11. Project 1 - Report
12. Expert systems
13. Expert systems