NWI-IMC042
Natural Computing
Course infoSchedule
Course moduleNWI-IMC042
Credits (ECTS)6
CategoryMA (Master)
Language of instructionEnglish
Offered byRadboud University; Faculty of Science; Informatica en Informatiekunde;
Lecturer(s)
Lecturer		drs. J. Acquarelli
Other course modules lecturer
Lecturer		prof. dr. E. Marchiori
Other course modules lecturer
Coordinator		prof. dr. E. Marchiori
Other course modules lecturer
Contactperson for the course		prof. dr. E. Marchiori
Other course modules lecturer
Academic year2017
Period
KW3-KW4  (05/02/2018 to 02/09/2018)
Starting block
KW3
Course mode
full-time
Remarks-
Registration using OSIRISYes
Course open to students from other facultiesYes
Pre-registrationNo
Waiting listNo
Placement procedure-
Aims
On completion of the course students should be able to:
  • Outline the main Natural Computing algorithms
  • Compare and Contrast different Natural Computing approaches
  • Solve a problem using Natural Computing
  • Design an experiment in Natural Computing
  • Write an academic paper on this subject
Content

The field of Natural Computing concerns the development of algorithms inspired by Nature, including Biological, Social and Physical systems. These algorithms draw metaphorical inspiration from diverse aspects of nature, including the operation of biological neurons, processes of evolution, and models of social interaction amongst organisms. They are used to tackle complex real-world problems. This course provides an introduction to Natural Computing algorithms and illustrates how they can be applied to real-world problems using case studies.

Literature
Scientific papers and tutorials.

Teaching formats

• 8 hours guided group project work
• 20 hours lecture
• 60 hours individual project work without guidance
• 8 hours student presentations
• 16 hours question session
• 64 hours individual study period

Extra information teaching methods: Home assignments, group work on a project, seminar presentations and reports.

Topics
Topics include evolutionary algorithms, particle swarm optimisation, ant colony optimisation, cellular automata, evolutionary game theory, deep neural networks.

Test information
Home assignments, group work on a project, seminar presentations and reports.

Prerequisites
Bachelor course "Data Mining".

Required materials
Articles
Scientific papers and tutorials.

Instructional modes
Course

General
Home assignments, group work on a project, seminar presentations and reports.

Lecture

Presentation
Attendance MandatoryYes

Project
Attendance MandatoryYes

Response course

Zelfstudie

Tests
Tentamen
Test weight1
OpportunitiesBlock KW4, Block KW4