Intelligent Analytics and Artificial Intelligence
level of course unit
Learning outcomes of course unit
Students understand the concept of artificial intelligence (AI). They are familiar with the basic underlying concepts and know/understand various implementation approaches for AI. Furthermore, they understand the significance of data and algorithms with respect to implementation and are able to independently implement simple applications.
prerequisites and co-requisites
Students learn about the fundamental techniques and concepts within the context of intelligent operating systems, specifically in the following areas:
-Reasoning (goal trees, rule-based expert systems)
-Search (depth-first, hill climbing, beam, optimal, branch and bound, A*, games, minimax and alpha-beta)
-Constraint (search, domain reduction, visual object recognition)
-Learn (neural nets, back propagation, genetic algorithms, sparse spaces, phonology, near misses, felicity conditions, support vector machines, boosting)
-Representation (classes, trajectories, transitions)
-Usage of AI within the context of business
recommended or required reading
- Runkler, T. A. (2016) Data Analytics: Models and Algorithms for Intelligent Data Analysis. 2. Auflage, Springer Vieweg, Wiesbaden (ISBN: 978-3658140748).
- Russell, S.; Norvig, P. (2016) Artificial Intelligence: A Modern Approach, Global Edition. 3. Auflage, Addison Wesley, Boston (ISBN: 978-1292153964).
- Winson, P. H. (1992) Artificial Intelligence. 3. Auflage, Pearson (ISBN: 978-0201533774).
assessment methods and criteria
Seminar thesis or final examination
language of instruction
number of ECTS credits allocated
planned learning activities and teaching methods
Lecture with discussion
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