Module 19341 (2010)
Syllabus page 2010/2011
06-19341
Introduction to Natural Computation
Level 2/I
Links | Outline | Aims | Outcomes | Prerequisites | Teaching | Assessment | Books | Detailed Syllabus
The Module Description is a strict subset of this Syllabus Page. (The University module description has not yet been checked against the School's.)
Relevant Links
Outline
Natural computation is the study of systems in nature that lend themselves to a computational interpretation. This module provides an introduction to the field, emphasising common themes, principles and techniques. It lays the foundations for further advanced study of specific areas (such as neural networks and evolutionary algorithms).
Aims
The aims of this module are to:
- introduce the field of natural computation
- explore common themes and principles underlying different natural computation systems
- provide a foundation for the further study of some specific techniques
Learning Outcomes
| On successful completion of this module, the student should be able to: | Assessed by: | |
| 1 | explain and illustrate the key concepts of: de-centralisation, interaction, self-organisation, emergence | Examination |
| 2 | describe the common principles underlying a range of natural computation techniques | Examination |
| 3 | compare and contrast natural systems with their computational counterparts | Examination |
| 4 | show how natural computation techniques can be adapted to solving learning and optimisation problems | Examination |
| 5 | analyse the behaviour of natural computation systems | Examination, continuous assessment |
Restrictions, Prerequisites and Corequisites
Restrictions:
None
Prerequisites:
None
Co-requisites:
None
Teaching
Teaching Methods:
2 hours of lectures per week
Contact Hours:
Assessment
- Sessional: 1.5 hour examination (70%), continuous assessment (30%).
- Supplementary (where allowed): 1.5 hour examination (100%).
Recommended Books
| Title | Author(s) | Publisher, Date |
| An Introduction to Neural Networks | Kevin Gurney | Routledge, 1997 |
| An Introduction to Genetic Algorithms | Melanie Mitchell | MIT Press, 2001 |
| Swarm Intelligence | James Kennedy & Russell Eberhart | Morgan Kaufmann, 2001 |
| Artificial Life: an overview | Christopher Langton | MIT Press, 1995 |
| On Growth, Form and Computers | S. Kumar & P. Bentley | Elsevier, 2003 |
| Self-Organization in Biological Systems | S. Camazine, J. Deneubourg, N. Franks, J. Sneyd, G. Theraulaz & E. Bonabeau | Princeton University Press, 2001 |
Detailed Syllabus
- Simple interaction models.
- Cellular automata, flocking.
- Interactions, games, co-operation.
- Diffusion models.
- Social behaviour (insects, humans).
- Networks of interaction.
- Evolution by Natural Selection.
- Genetic algorithms.
- Gene regulation and cell signaling.
- Neuronal interactions.
- Perceptrons.
- Other neural models.
Last updated: 24 Sep 2009
Source file: /internal/modules/COMSCI/2010/xml/19341.xml
Links | Outline | Aims | Outcomes | Prerequisites | Teaching | Assessment | Books | Detailed Syllabus