level of course unit
Learning outcomes of course unit
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.
prerequisites and co-requisites
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.
recommended or required reading
- Winson, P. H. (1992): Artificial Intelligence (Ed. 3), Pearson, (ISBN: 978-0201533774)
- Russell, S.; Norvig, P. (2016): Artificial Intelligence: A Modern Approach, Global Edition (Ed. 3), Addison Wesley, Boston (ISBN: 978-1292153964)
assessment methods and criteria
language of instruction
number of ECTS credits allocated
eLearning quota in percent
planned learning activities and teaching methods
The following methods are used:
- Lecture with discussion
- Interactive workshop
semester/trimester when the course unit is delivered
name of lecturer(s)
Dr. Dipl.-Ing. Dietmar Millinger
course unit code
type of course unit
mode of delivery