Course detail

Artificial Intelligence

FEKT-NUINAcad. year: 2011/2012

The aim of the course is to deepen knowledges and application of artificial intelligence methods. Artificial intelligence. Neural networks, paradigm, backpropagation algorithm,
neural networks as associative memories, RCE neural network, Kohonen maps. Expert systems, principle. Knowledge reprezentation. Problem solving.

Language of instruction

English

Number of ECTS credits

6

Mode of study

Not applicable.

Guarantor

doc. Ing. Václav Jirsík, CSc.

Department

Department of Control and Instrumentation (UAMT)

Learning outcomes of the course unit

To acquaint the students with the methods of problem solving, knowledge reprezentation and fundamentals of artificial neural networks.

Prerequisites

The subject knowledge on the Bachelor´s degree level is requested.

Co-requisites

Not applicable.

Planned learning activities and teaching methods

Teaching methods depend on the type of course unit as specified in the article 7 of BUT Rules for Studies and Examinations.

Assesment methods and criteria linked to learning outcomes

Work of students is evaluated during study by tests in exercises and one control test. They can obtain maximum 30 points by these tests during semester.
Final examination is evaluated by 70 points at maximum.

Course curriculum

Artificial intelligence
Neural networks
Knowledge representation
Problem solving
Expert systems
Computer vision

Work placements

Not applicable.

Aims

To acquaint the students with fundamentals of artificial intelligence, representation and general theory of artificial neural networks and problem solving.

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

The content and forms of instruction in the evaluated course are specified by a regulation issued by the lecturer responsible for the course and updated for every academic year.

Recommended optional programme components

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

Not applicable.

Recommended reading

Not applicable.

Classification of course in study plans

  • Programme EECC-MN Master's

    branch MN-KAM , 2 year of study, winter semester, compulsory
    branch MN-EEN , 2 year of study, winter semester, elective interdisciplinary
    branch MN-TIT , 1 year of study, winter semester, elective interdisciplinary

Type of course unit

 

Lecture

39 hod., optionally

Teacher / Lecturer

doc. Ing. Václav Jirsík, CSc.

Syllabus

Artificial intelligence,definition,methods
Neural networks, biological neural units
Model of neurons and paradigm of neuron nets
Multilayer perceptrons, backpropagation algorithm, modified algorithms BP
Neural networks as associative memories, RCE neural network, Kohonen maps
Expert systems, principle, structure
Knowledge reprezentation, logic, production rules
Knowledge reprezentation, semantic nets, frames
Problem solving, type of problems, heuristic
Problem solving methods
Methods of inference
Speech recognition, processing, simulation and synthesis of speech
Speech recognition, methods of pattern recognition, using for instruments

Exercise in computer lab

26 hod., optionally

Teacher / Lecturer

doc. Ing. Václav Jirsík, CSc.

Syllabus

Matlab with Simulink
Backpropagation algorithm modelling 1
Backpropagation algorithm modelling 2
Dynamic system modelling by neural network
Sensitivity analysis of neural networks
Pattern recognition by neural networks
Expert systems application