Course detail
Intelligent Systems
FIT-ISDAcad. year: 2017/2018
Tolerance of imprecision and uncertainty as main attribute of ISY. Intelligent systems based on combinations of several theories - neural networks, fuzzy sets, rough sets and genetic algorithms: expert systems, intelligent information systems, machine translation systems, intelligent sensor systems, intelligent control systems, intelligent robotic systems.
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Learning outcomes of the course unit
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Planned learning activities and teaching methods
Assesment methods and criteria linked to learning outcomes
Course curriculum
- Syllabus of lectures:
- Introduction, soft computing and ISY
- Expert systems
- Intelligent information systems
- Machine translation systems
- Surrounding environment perception, intelligent sensor systems
- Analysis of sensor data, environment model design
- Planning of given tasks accomplishments
- Control systems with neural networks
- Fuzzy control systems
- Neuro-fuzzy systems
- Utilization of rough sets and genetic algorithms in ISY
- Intelligent robotic systems
- Navigation of mobile robots
- Individual projects - designs of intelligent systems for solving some practical problem
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Syllabus - others, projects and individual work of students:
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Specification of controlled education, way of implementation and compensation for absences
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Basic literature
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Classification of course in study plans
Type of course unit
Lecture
Teacher / Lecturer
Syllabus
- Introduction, soft computing and ISY
- Expert systems
- Intelligent information systems
- Machine translation systems
- Surrounding environment perception, intelligent sensor systems
- Analysis of sensor data, environment model design
- Planning of given tasks accomplishments
- Control systems with neural networks
- Fuzzy control systems
- Neuro-fuzzy systems
- Utilization of rough sets and genetic algorithms in ISY
- Intelligent robotic systems
- Navigation of mobile robots