The information revolution is leading to unprecedented volumes of stored data, representing a gold mine of useful information with the potential to impact on many fields such medicine, finance, and scientific research. The Learning from Data specialism provides a comprehensive grounding in key techniques such as machine learning, probabilistic inference, pattern recognition and neural networks, and will leave students well placed for postgraduate research towards a PhD or for industrial employment.
Some interesting applications of such methods are:
Further details on these applications can be found
Students who are primarily interested in Learning from Data can choose to specialise just in this area. The Learning from Data specialism comprises eight taught modules, plus a five month long research project. Of the eight modules, there are three compulsory inner core modules: Learning from Data 1, Probabilistic Modelling and Reasoning, Data Mining and Exploration. Students must also take two modules from Learning from Data 2, Genetic Algorithms and Genetic Programming, Visualization, Applied Databases, Data Intensive Linguistics. The remaining modules can be chosen from the whole range of MSc modules, although we would normally expect one of them to be a progamming module appropriate to the student's prior programming experience. Further details on the modules can be obtained from the webpage for current MSc students.
We have a number of EPsrc grants available to support UK students who have a very good honours degree (or equivalent qualification) in a numerate area such as Computer Science, Physics etc. (Partial support may be available for EU students.) Please indicate on your application form if you wish to be considered for this course, which would lead to an MSc in Informatics degree.
Informatics Forum, 10 Crichton Street, Edinburgh, EH8 9AB, Scotland, UK
Tel: +44 131 651 5661, Fax: +44 131 651 1426, E-mail: email@example.com
Please contact our webadmin with any comments or corrections. Logging and Cookies
Unless explicitly stated otherwise, all material is copyright © The University of Edinburgh