Artificial Intelligence

Description

After succesful completion of this course, a student will have deep knowledge and insight in a limited number of basic techniques from artificial intelligence, including: basic search methods, heuristic search methods, optimal path search methods, optimal path search methods with limited memory use, game search techniques, version spaces for machine learning and data mining, backtracking techniques for constraint processing, consistency techniques for constrain processing, hybrid techniques for constraint processing, Bayesian networks, Markov decision processes, elements of machine learning and neural networks,  and general resolution theorem proving for first order predicate logic, be able to simulate each of the above techniques with pen and paper on small new examples, have insight of the relevance of these techniques for applications, be able to use provided didactic software tools to develop solutions on a computer for less small examples, have insight in the relations between these techniques.