Course: Introduction to Artificial Intelligence

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Course title Introduction to Artificial Intelligence
Course code KRP/INUI2
Organizational form of instruction Lecture + Lesson
Level of course Master
Year of study not specified
Semester Summer
Number of ECTS credits 5
Language of instruction Czech
Status of course Compulsory-optional
Form of instruction Face-to-face
Work placements This is not an internship
Recommended optional programme components None
Lecturer(s)
  • Gago Lumír, Ing.
  • Taufer Ivan, prof. Ing. DrSc.
  • Doležel Petr, doc. Ing. Ph.D.
  • Mariška Martin, Ing.
Course content
1. Introduction. Basics and Definitions. History. 2. Biological Neural Network. Biological Neuron. 3. Artificial Neural Network. Artificial Neuron. Perceptron. 4. Artificial Neural Network Training and Testing. 5. Classes of Artificial Neural Networks. 6. Feedforward Multilayer Artificial Neural Networks. 7. Backpropagation Gradient Descent Training Algorithm. 8. Static Modelling. Training and Testing Sets. 9. Dynamic Modelling. Training and Testing Set. 10. Process Control Using Artificial Neural Networks. 11. Genetic Algorithms. 12. Genetic Algorithms Applications. 13. Motivation for Artificial Intelligence Applying. Practical Implementations

Learning activities and teaching methods
Monologic (reading, lecture, briefing), Dialogic (discussion, interview, brainstorming), Methods of individual activities, Laboratory work
Learning outcomes
The goal of the subject is to teach the students modern methods how to model static and dynamical properties of the system. Students will learn neural network paradigm, theoretical background and practical implementation.
Student will be able to create artificial neural networks and realize computationally their learning and implementation.
Prerequisites
Static and dynamical properties description. Differential and difference equations solution. Basics of continuous- and discrete-time modelling of the processes. Basic knowledge of the computational system MATLAB/SIMULINK.

Assessment methods and criteria
Oral examination, Home assignment evaluation

Seminar lessons attendance. Written seminar work.
Recommended literature
  • ČSN ISO/IEC 2382-34. Informační technologie - Slovník - část 34: Umělá inteligence - neuronové sítě.. Praha : ČNI, 2001.
  • Novák, Mirko . Umělé neuronové sítě : teorie a aplikace. Praha: C.H. Beck, 1998. ISBN 80-7179-132-6.
  • Sinčák, P.; Andrejková, G. Neurónové siete. Inžiniersky prístup. 1. a 2. diel. Košice : elfa, s.r.o., 1996. ISBN 80-88789-42-8.
  • Šíma, J.; Neruda, R. Teoretické otázky neuronových sítí.. Praha : MATFYZPRESS, 1996. ISBN 80-85863-18-9.


Study plans that include the course
Faculty Study plan (Version) Branch of study Category Recommended year of study Recommended semester
Faculty of Electrical Engineering and Informatics Information Technology (2016) Informatics courses 1 Summer
Faculty of Electrical Engineering and Informatics Communication and Controlling Technology (2014) Electrical engineering, telecommunication and IT 1 Summer
Faculty of Electrical Engineering and Informatics Process Control (2013) Special and interdisciplinary fields - Summer
Faculty of Electrical Engineering and Informatics Communication and Controlling Technology (2015) Electrical engineering, telecommunication and IT 1 Summer
Faculty of Electrical Engineering and Informatics Process Control (2014) Special and interdisciplinary fields - Summer
Faculty of Electrical Engineering and Informatics Information Technology (2015) Informatics courses 1 Summer
Faculty of Electrical Engineering and Informatics Information Technology (2014) Informatics courses 1 Summer
Faculty of Electrical Engineering and Informatics Communication and Controlling Technology (2016) Electrical engineering, telecommunication and IT 1 Summer
Faculty of Electrical Engineering and Informatics Process Control (2015) Special and interdisciplinary fields - Summer
Faculty of Electrical Engineering and Informatics Process Control (2016) Special and interdisciplinary fields - Summer