MRes Natural Computation - 2020
|Programme Title||Natural Computation|
|School/Department||School of Computer Science|
|Length of Programme||1 years|
|Awarding Institution||The University of Birmingham|
|QAA Benchmarking Groups||Computing|
Educational Aims Of Programme
- Meet the increasing need from industry for graduates equipped with a knowledge of natural computation techniques.
- Provide a solid foundation in natural computation for graduates to pursue a research and development career in industry or to pursue further studies (e.g. PhD).
- Give up-to-date coverage of current topics in natural computation (such as evolutionary algorithms, co-evolution, evolutionary design, nature-inspired optimisation techniques, evolutionary games, novel learning algorithms, artificial neural networks, the theory of natural computation).
Programme Outcomes and Learning, Teaching and Assessment Strategies
Knowledge and Understanding
- The ideas and concepts underlying a variety of natural computation techniques.
- The strength and weakness of natural computation techniques and the similarities and differences between these techniques and other existing techniques.
- Applications of these techniques to solve large and complex problems in industry.
Skills & Other Attributes
- Given a real-world problem, identify whether natural computation techniques are likely to provide a good solution and then select appropriate techniques from those available.
- Derive efficient solutions to appropriate non-trivial problems using natural computation techniques alone or in combination with other techniques and implement these solutions using both 'off the shelf' software and software they themselves write.
- By the end of the programme, carry out original research and development (e.g. in industry or for a subsequent PhD) with limited supervision, both in the field of natural computation and in allied fields
- Use appropriate methods for literature searching and information retrieval
- Know and apply a variety of research methods, including experimental design and the analysis of data