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

Czech

Number of ECTS credits

5

Mode of study

Not applicable.

Entry knowledge

Not applicable.

Rules for evaluation and completion of the course

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

Aims

The course aims to explain the basic concepts of artificial intelligence (such as AI, ANI, ML, AGI, and ASI), with special emphasis on artificial neural networks, knowledge systems, and computer vision.
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

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 (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)
DUDA, Richard, HART Peter a STORK David. Pattern Classification. New York: John Wiley & Sons, INC. 2001. 654 s. ISBN 0-471-05669-3. (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

26 hod., optionally

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

26 hod., compulsory

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