Course detail
Artificial Intelligence
FEKT-MUINAcad. year: 2012/2013
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
Number of ECTS credits
Mode of study
Guarantor
Learning outcomes of the course unit
Prerequisites
Co-requisites
Planned learning activities and teaching methods
Assesment methods and criteria linked to learning outcomes
Final examination is evaluated by 70 points at maximum.
Course curriculum
Neural networks
Knowledge representation
Problem solving
Expert systems
Work placements
Aims
Specification of controlled education, way of implementation and compensation for absences
Recommended optional programme components
Prerequisites and corequisites
Basic literature
Recommended reading
Classification of course in study plans
- Programme EEKR-M Master's
branch M-EEN , 2 year of study, winter semester, elective interdisciplinary
branch M-TIT , 1 year of study, winter semester, elective interdisciplinary
branch M-KAM , 2 year of study, winter semester, compulsory - Programme EEKR-M Master's
branch M-EEN , 2 year of study, winter semester, elective interdisciplinary
branch M-TIT , 1 year of study, winter semester, elective interdisciplinary
branch M-KAM , 2 year of study, winter semester, compulsory - Programme EEKR-CZV lifelong learning
branch EE-FLE , 1 year of study, winter semester, compulsory
Type of course unit
Lecture
Teacher / Lecturer
Syllabus
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
Teacher / Lecturer
Syllabus
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