Artificial Intelligence

Master's course

The following skills are developed in the course: - Students are familiar with different strategies for the implementation of artificially intelligent systems. - Students understand the advantages and disadvantages of the strategies developed and are aware of their chal-lenges. - Students can develop strategies to design artificially intelligent systems for practical use.

No prerequisites

The following content is discussed in the course: - Reasoning approaches (Roal trees, rule-based expert systems) - Search approaches (depth-first, hill climbing, beam, optimal, branch and bound, A*, games, minimax, and alpha-beta) - Constraint approaches (search, domain reduction, visual object recognition) - Learning approaches (neural nets, back propagation, genetic algorithms, sparse spaces, phonology, near misses, felicity conditions, support vector machines, boosting) - Representation approaches (classes, trajectories, transitions) - Possible applications of artificial intelligence in different contexts - Weak versus strong, artificial intelligence This course is offered together with the Web Communication and Information Systems Master program as an elective course.

PRIMARY LITERATURE: - Winson, P. H. (1992): Artificial Intelligence (Ed. 3), Pearson, (ISBN: 978-0201533774) SECONDARY LITERATURE: - Russell, S.; Norvig, P. (2016): Artificial Intelligence: A Modern Approach, Global Edition (Ed. 3), Addison Wesley, Boston (ISBN: 978-1292153964)

Written exam

English

4

25

2.0

The following methods are used: - Lecture with discussion - Interactive workshop

3

Dr. Dipl.-Ing. Dietmar Millinger

DPR.9

integrated lecture

Compulsory

none