Artificial Intelligence

NUMBER: 310502
SHORT: KI
MODULBEAUFTRAGTE:R: Prof. Dr. Asja Fischer
LECTURER: Prof. Dr. Asja Fischer
FACULTY: Fakultät für Informatik
LANGUAGE: English
SWS: 4 SWS
CREDITS: 5 CP
WORKLOAD: 150 h
OFFERED IN: each summer semester

BESTANDTEILE UND VERANSTALTUNGSART

Artificial Intelligence – Lecture (2 SWS)
Artificial Intelligence – Exercise (2 SWS)

EXAMS

FORM: schriftlich
ANMELDUNG: eCampus
DATE: 0000-00-00
START: 00:00:00
DURATION: 120 min
ROOM:

LERNFORM

The lecture will be held as a seminar with media support, eLearning-supported homework with practical exercises to be implemented on the computer are assigned weekly and discussed in the exercise lesson.

LERNZIELE

After successfully completing the module
- students understand the basic procedures and methods of artificial intelligence and can apply them in practice
- the students are able to assess the efficiency of the discussed procedures and can use them successfully to solve specific problems of different application domains.
- the students have mastered the terminology of the subject area
- the students know both industrially and economically relevant fields of application

CONTENT

The lecture gives an overview of important approaches and methods of artificial intelligence. In terms of content, important concepts and ideas such as design principles of intelligent agents, problem solving through search and knowledge-based inference, problem solving in the case of uncertain knowledge, action planning and basic ideas of machine learning are covered. Furthermore, important areas of application and possible uses in practice are to be learned.

REQUIREMENTS CREDITS

Passed final module examination and successful participation in the exercises

RECOMMENDED PRIOR KNOWLEDGE

Solid basic knowledge in mathematics (content of the modules Mathematics 1 - Basics, Mathematics 2 - Algorithms, Mathematics 3 - Applications) and in computer science (content of the modules Computer Science 1 - Programming and Computer Science 2 - Algorithms and data structures)

LITERATURE

1. S. Ruseel und P. Norvig: „Artificial Intelligence: A Modern Approach“, Prentice Hall,
3rd edition, 2009
2. W. Ertel: „Grundkurs Künstliche Intelligenz: Eine praxisorientierte Einführung“, 4. Auflage, 2016

MISC INFORMATION

Current information such as lecture dates, rooms or current lecturers and instructors can be found in the course directory of the Ruhr-Universität https://vvz.rub.de/ and in the eCampus https://www.rub.de/ecampus/ecampus-webclient /