Artificial Intelligence
Description
This course aims at providing insight in the fundamental concepts of the theory and applications in the broad Artificial Intelligence discipline. An overview of the most commonly used methods and models is presented, of which a number are treated in depth. Especially, focus is put on the topic Machine Learning and data driven model building, also addressing the limitations of these approaches. The course also investigates search and decision problems.
Final competences
- Having an overall view on the different generic problem classes in the AI discipline
- Having insight in the fundamentals and concepts underlying commonly used solution techniques in this discipline, especially focussing on data driven model construction (white box as well as black box)
- Having a thorough understanding of search strategies, focussing on decision problems (Markovian Decision Problems and the connection to Reinforcement Learning, planning problems in dynamical environments).
- Solving specific problems in AI using the methods of this course (and extending these methods as needed in terms of applicability and context), as well on paper as in Python.
- Being able to assess the limitations and ethical consequences of AI-techniques.
More info -> www.ugain.ugent.be/MCAI2026.htm
