Intelligent Systems

Selected topics will be announced at the start of the semester and may include some of the following:

• Introduction to artificial intelligence
• Rule-based expert systems
• Frame-based systems
• Fuzzy logic and fuzzy expert systems
• Agent-based modelling and simulation
• Evolutionary algorithms
• Machine learning
• Artificial neural networks
• Reinforcement learning
• Hybrid intelligent systems
• Possibly other topics

Knowledge

• The candidate can explain and compare theory, principles, applications, strengths and weaknesses of methods presented in the course

Skills

• The candidate can demonstrate the use of methods presented in the course, both through digital tools and simulation

General competence

• The candidate can use digital tools for implementation of intelligent systems
• The candidate can explain the value of intelligent systems for sustainable processes, services, or systems
• The candidate can present problems and relevant solution methods in a professional and scientific manner
• The candidate can discuss ethical challenges of artificial intelligence

Learning activities generally include a mix of lectures, tutorials and practical assignment or project work. A constructivist and contextual approach for learning is endorsed, with focus on problem solving and practical application of theory.