inf535 - Computational Intelligence I (Complete module description)

Module label	Computational Intelligence I
Module code	inf535
Credit points	6.0 KP
Workload	180 h
Institute directory	Department of Computing Science
Applicability of the module	Master Applied Economics and Data Science (Master) > Data Science Master's Programme Business Informatics (Master) > Akzentsetzungsmodule der Informatik Master's Programme Computing Science (Master) > Angewandte Informatik Master's Programme Engineering of Socio-Technical Systems (Master) > Embedded Brain Computer Interaction Master's Programme Engineering of Socio-Technical Systems (Master) > Human-Computer Interaction Master's Programme Engineering of Socio-Technical Systems (Master) > Systems Engineering Master's Programme Environmental Modelling (Master) > Mastermodule
Responsible persons	Kramer, Oliver (module responsibility) Lehrenden, Die im Modul (authorised to take exams)
Prerequisites	Basics of statistics
Skills to be acquired in this module	After successful completion of the course, students should have acquired the ability to master the presented methods in theory and practice. The students should be able to recognize and model corresponding optimization and data analysis problems themselves and to apply the methods unerringly. Professional competence The students: recognise optimisation problems implement simple algorithms of heuristic optimisation critically discuss solutions and selection of methods deepen previous knowledge of analysis and linear algebra Methodological competence The students: deepen programming skills apply modelling skills learn about the relation between problem class and method selection Social competence The students: cooperatively implement content introduced in lecture evaluate own solutions and compare them with those of their peers Self-competence The students: evaluate own skills with reference to peers realize personal limitations adapt own problem solving approaches with reference to required method competences
Module contents	Computational Intelligence comprises intelligent and adaptive methods for optimisation and learning. The module "Computational Intelligence I" concentrates on methods for evolutionary optimisation and heuristic approaches. The exercises introduce and deepen practical aspects of the implementation and algorithmic design, also taking into account application aspects. Overview of Content: foundations of optimisation genetic algorithms and evolution strategies parameter control and self-adaptation runtime analysis swarm algorithms constrained optimisation multi-objective optimisation meta-modeling
Recommended reading	EIBEN, A. E.; SMITH, J. E.: Introduction to Evolutionary Computing. Springer, 2003. KENNEDY, J.; EBERHART, R.C.; YUHUI, S.: Swarm Intelligence. Morgan Kaufmann, 2001. KRAMER, O.: Computational Intelligence. Springer, 2009. RUTKOWSKI, L.: Computational Intelligence Methods and Techniques. Springer, 2008. ROJAS, R.: Theorie der neuronalen Netze: Eine systematische Einführung. Springer, 1993.
Links
Languages of instruction	English , German
Duration (semesters)	1 Semester
Module frequency	annual
Module capacity	unlimited
Teaching/Learning method	V+Ü

Type of course	SWS	Frequency	Workload of compulsory attendance
Lecture	2	WiSe	28
Exercises	2	WiSe	28
Total module attendance time			56 h

Examination	Prüfungszeiten	Type of examination
Final exam of module	At the end of the lecture period	Written or oral exam