Stud.IP Uni Oldenburg
University of Oldenburg
31.10.2020 15:10:10
inf536 - Computational Intelligence II (Complete module description)
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Module label Computational Intelligence II
Module code inf536
Credit points 6.0 KP
Workload 180 h
Faculty/Institute Department of Computing Science
Used in course of study
  • 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 Environmental Modelling (Master) > Mastermodule
Contact person
Module responsibility
Authorized examiners
Entry requirements
Skills to be acquired in this module
Professional competence
The students:
  • Recognise machine learning problems
  • Implement simple algorithms of machine learning
  • 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

The students:
  • Evaluate own skills w.r.t. peers
  • Realise personal limitations
  • Adapt own problem solving approaches w.r.t. required method competences
Module contents
Computational Intelligence comprises intelligent and adaptive methods for optimisation and learning. The module "Computational Intelligence II" concentrates on methods for machine learning and data mining. The exercises introduce and deepen practical aspects of the implementation and algorithmic design, also taking into account application aspects.

Overview of Content:
  • Foundations of learning and classification
  • Nearest neighbouring methods
  • Model selection and parameter tuning
  • Regression
  • Support vector and kernel methods
  • Clustering
  • Dimensionality reduction
Reader's advisory
  • BISHOP, C.M.: Pattern Recognition and Machine Learning. Springer 2007.
  • HASTIE, T., TIBSHIRANI, R., FRIEDMAN, J.H.: The Elements of Statistical Learning, Springer 2009
Languages of instruction German, English
Duration (semesters) 1 Semester
Module frequency once a year
Module capacity unlimited
Modullevel AS (Akzentsetzung / Accentuation)
Modulart je nach Studiengang Pflicht oder Wahlpflicht
Lern-/Lehrform / Type of program V+Ü
Vorkenntnisse / Previous knowledge - inf535 Computational Intelligence I
- Statistik
Course type Comment SWS Frequency Workload attendance
Lecture 2.00 SuSe 28 h
Exercises 2.00 SuSe 28 h
Total time of attendance for the module 56 h
Examination Time of examination Type of examination
Final exam of module
At the end of the semester
Written or oral exam