Advanced Topics in Statistics - 2020 entry

This module offers an insight to cutting-edge statistical learning techniques that are at the forefront of current research and application. You will have opportunity to explore a range of topics including time series modelling and forecasting, decision trees, random forests, support vector machines, neural networks and Bayesian computation. The choice of topics in any year may change to ensure that the content of the module reflects the rapid change in this exciting area.

The aims are to expose the student to some recent developments in statistics; to allow the student to study one or more advanced topics in some depth.

On successful completion of this module, you should be able to:

Module Specific Skills and Knowledge:

1 Demonstrate an understanding of current developments in statistics;

2 Demonstrate an understanding of the strengths and limitations of different statistical approaches;

3 Demonstrate the ability to apply advanced statistical methodology across a variety of settings;

Discipline Specific Skills and Knowledge:

4 Demonstrate an understanding of advanced regression modelling;

5 Demonstrate an understanding of modelling data with dependence;

6 Demonstrate the ability to self-learn further details of the methodology introduced within topics;

Personal and Key Transferable/ Employment Skills and Knowledge:

7 Statistical analysis skills;

8 Self-learning and making effective use of learning resources;

9 Effective use of learning resources;

10 Report writing and presentation.

The syllabus will depend upon the module topic(s) offered and will be specified in detail by the lecturer(s) and agreed by the module coordinator for any particular year. Examples of topics include time series modelling and forecasting, decision trees, random forests, support vector machines, neural networks and Bayesian computation. Other suitable topics may also be offered.

As above

Deferral – if you miss an assessment for certificated reasons judged acceptable by the Mitigation Committee, you will normally be either deferred in the assessment or an extension may be granted. The mark given for a re-assessment taken as a result of deferral will not be capped and will be treated as it would be if it were your first attempt at the assessment.

Referral – if you have failed the module overall (i.e. a final overall module mark of less than 50%) you will be required to re-take some or all parts of the assessment, as decided by the Module Convenor. The final mark given for a module where re-assessment was taken as a result of referral will be capped at 50%.

Basic Reading:

ELE: http://vle.exeter.ac.uk/

Reading list for this module: