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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