Course detail

Artificial Intelligence

FEKT-MPC-UINAcad. 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

Czech

Number of ECTS credits

5

Mode of study

Not applicable.

Learning outcomes of the course unit

Course graduate should be able to:
- 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

The subject knowledge on the Bachelor´s degree level is requested and knowledge about programming MATLAB.

Co-requisites

Not applicable.

Planned learning activities and teaching methods

Techning methods include lectures and computer laboratories. Students have to write a single project during the course.

Assesment methods and criteria linked to learning outcomes

Condition of petting full credit is absolut (100%) attendance in obligatory parts of lessons - the computer exercises and obtaining at least 15 points. Students are tested continuously and i tis possible to get maximum 20 points. The final written exam is rated by 70 points at maximum and the oral exam is rated by 10 points at maximum.

Course curriculum

1. Artificial intelligence - definition, history, area
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

Not applicable.

Aims

The aim of this course is to provide students with a basic orientation in key algorithms and artificial inteligence, emphasis is placed on the field of artificial neural networks, knowledge systems and computer vision.

Specification of controlled education, way of implementation and compensation for absences

The computer exercises are compulsory, the properly excused missed computer exercises can be compensate.

Recommended optional programme components

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

MAŘÍK, Vladimír, ŠTĚPÁNKOVÁ, Olga, LAŽANSKÝ, Jiří a kolektiv. Umělá inteligence (1. až 6. díl) Praha: Academia 1993 - 2013. (CS)
RUSESELL, Stuart a NORVIG, Peter. Artificial Intelligence. A Modern Aproach. New Jersey: Prentice Hall 2010. 1132 s. ISBN-13: 978-0-13-604259-4. (EN)

Recommended reading

DUDA, Richard, HART Peter a STORK David. Pattern Classification. New York: John Wiley & Sons, INC. 2001. 654 s. ISBN 0-471-05669-3. (CS)
SONKA, Milan, HLAVAC, Vaclav a BOYLE, Rogert. Image Processing, Analysis and Machine Vision. Toronto: Thomson, 2008. 829 s. ISBN 978-0-495-24438-7. (CS)

Classification of course in study plans

  • Programme MPC-AUD Master's

    specialization AUDM-TECH , 1 year of study, winter semester, compulsory-optional
    specialization AUDM-ZVUK , 1 year of study, winter semester, compulsory-optional

  • Programme EEKR-CZV lifelong learning

    branch EE-FLE , 1 year of study, winter semester, compulsory-optional

Type of course unit

 

Exercise in computer lab

26 hod., compulsory

Teacher / Lecturer

Syllabus

1. Introduction, projects - assignment
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

Lecture

26 hod., optionally

Teacher / Lecturer

Syllabus

1. Artificial intelligence - definition, history, area
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