Module 12417 (2001)
Syllabus page 2001/2002
06-12417
Nature Inspired Learning
Level M1
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
See Nature
Inspired Learning Web-page for course material and further
useful links.
Outline
This module gives an introduction to the main learning techniques inspired by natural, physical and biological systems. Examples of such topics include: learning classifier systems, competitive and cooperative co-evolution, evolutionary games, learning through genetic programming, support vector machines, adaptive resonance theory neural networks, Boltzmann machines, interaction between learning and evolution, Baldwin effect, Lamarckian evolution, memetic algorithms, and the evolution of neural networks.
Aims
The aims of this module are to:
- present a range of nature inspired learning techniques
- show how these techniques can be applied to particular problems
Learning Outcomes
| On successful completion of this module, the student should be able to: | Assessed by: | |
| 1 | demonstrate a good understanding of the nature inspired learning techniques presented in the module, and the relations between them | Examination |
| 2 | demonstrate an understanding of the benefits and limitations of nature inspired learning algorithms | Examination |
| 3 | apply nature inspired learning algorithms to specific technical and scientific problems | Examination |
Restrictions, Prerequisites and Corequisites
Restrictions:
None
Prerequisites:
None
Co-requisites:
06-12411 (Introduction to Molecular and Quantum Computation), 06-12412 (Introduction to Neural Computation), 06-12414 (Introduction to Evolutionary Computation)
Teaching
Teaching Methods:
2 hrs/week lectures/tutorials
Contact Hours:
Assessment
- Supplementary (where allowed): As the sessional assessment
- 2 hr open book examination (100%).
Recommended Books
| Title | Author(s) | Publisher, Date |
| Evolutionary Computation: The Fossil Record | D.B. Fogel (Ed.) | IEEE Press, 1998 |
| Evolutionary Computation | K.A. De Jong | MIT Press, 2001 |
| Neural Networks for Pattern Recognition | C.M. Bishop | Oxford University Press, 1995 |
| The Handbook of Brain Theory and Neural Networks | M.A. Arbib (Ed.) | MIT Press, 1995 |
| Adaptive Individuals in Evolving Populations: Models and Algorithms | R. K. Belew & M. Mitchell (eds) | Addison-Wesley, 1996 |
Detailed Syllabus
- Overview of what Nature Inspired Learning covers
- Learning Classifier Systems
- Competitive and Cooperative Co-evolution - Red Queen Effect
- Evolutionary Games
- Learning through Genetic Programming
- Learning from Corpus Analysis - Bootstrapping Semantic Categories
- Support Vector Machines
- Adaptive Resonance Theory Neural Networks - ART1, ART2, ART3, ARTMAP, Fuzzy ART and ARTMAP, Distributed ART and ARTMAP
- Boltzmann Machines
- Interaction between Learning and Evolution - Baldwin effect, Lamarckian evolution, memetic algorithms
- Evolving Neural Networks - Individual Differences, Variable Plasticity, Modularity
Last updated: 29 July 2001
Source file: /internal/modules/COMSCI/2001/xml/12417.xml
Links | Outline | Aims | Outcomes | Prerequisites | Teaching | Assessment | Books | Detailed Syllabus