COMPUTING SCIENCE AND MATHEMATICS University | Computing Science | Big Data Msc. | CSCU9YO | Syllabus Updated 9/01/15 10:19
CSCU9YO Spring 2015

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Organisation

Materials

Assessment

Reference

## CSCU9YO Syllabus

### Credits

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### Learning Outcomes

The student should know and understand:

• Basic concepts and concerns surrounding optimization
• A number of key algorithms including single point search and population based methods such as genetic algorithms
• Typical problem domains and applications
• Some of the difficulties in research in optimization

### Transferable Skills

• Students should be able to understand problems from an objective perspective.
• Students should be able to make informed decisions about the design and evaluation of optimization algorithms and algorithms more widely
• Students should understand some of the open questions and difficulties surrounding the areas of research into metaheuristics and optimization

### Contents

• Mathematical definitions
• Optimization; means, methods and results
• Single point search methods e.g. simulated annealing
• Population based methods e.g. genetic algorithms
• Applications
• Theoretical Foundations
Students should also be able to demonstrate the ability to apply theory and techniques to unseen problems without reference to notes, to work independently and under a time constraint.

### Assessment

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### Handbook

 Coordinator , Room Email - Tel 01786 46 - Fax 01786 464551 Mail Computing Science and Mathematics, University of Stirling, Stirling, Scotland, FK9 4LA