Artificial Intelligence - Methods and Applications, 7.5 Credits

About the course

The course provides theoretical and methodological knowledge and skills in classical AI (artificial intelligence) and robotics.

Topics covered: Heuristics for search. Search for games. Applied predicate logic. Classical planning: heuristics. Knowledge representation. Probability theory: axioms, conditional probability, Bayes' rule. Bayesian networks. Probabilistic reasoning over time, Hidden Markov Models. Decision trees and learning. Robotics: reinforcement learning, learning from demonstration, hybrid architectures, motion planning, odometry, metric and topological route planning, localization and map generation.

Level of Education: Advanced

Notes: The course can be included in the Master's Program in Computational Science and Engineering

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Contact Information

Link to the course web page

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Department of Computing Science

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Yvonne Löwstedt

Tel: +46 90 786 5598

Fax: +46 90 786 61 26

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