Data Science & Intelligent Analytics PT
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Artificial Intelligence

level of course unit

Master's course

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

No prerequisites

course contents

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

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)

assessment methods and criteria

Written exam

language of instruction

English

number of ECTS credits allocated

4

eLearning quota in percent

25

course-hours-per-week (chw)

2

planned learning activities and teaching methods

The following methods are used:

- Lecture with discussion
- Interactive workshop

semester/trimester when the course unit is delivered

3

name of lecturer(s)

Prof. (FH) Dipl.-Informatiker Karsten Böhm

course unit code

DPR.9

type of course unit

integrated lecture

mode of delivery

Compulsory

work placement(s)

none