Fundamentals of Artificial Intelligence

Description

After successful completion of this course, a student will have deep knowledge and insight in a limited number of basic techniques from Artificial Intelligence, including: uninformed search methods, (basic and advanced) informed search methods, adversarial search methods used in games, version spaces machine learning, frequent pattern mining, (basic and advanced) backtracking techniques for constraint processing, Markov decision processes for probabilistic planning and techniques for automated reasoning; be able to simulate each of the above techniques with pen and paper on small new examples; be able to use the provided didactic software tools to develop solutions on a computer for less small examples; have insight into the relevance of these techniques for applications, in domains such as manufacturing, health, education, logistics, manufacturing, robotics; have insight in the relations between these techniques; have a basic understanding of the ethical implications of Artificial Intelligence.