COMPUTING SCIENCE
AND MATHEMATICS

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

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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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Requirements

Attendance

Plagiarism

Handbook

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