On successful completion of this module students should be able to
Understand the theory and be able to apply the following techniques to a set of data;
Classification trees;
Neural Networks;
Association rules;
Ensemble methods;
Random Forests;
RuleFit procedure (Jerome Friedman)
Support vector machines
Evaluation of models
Module Content
Handling missing data;
Detailed discussion of Classification Trees;
Detailed discussion of Evaluation of Models;
Overview of Association Rules;
Overview of Neural Nets;
Overview of Support vector machines;
Ensemble methods;
General Overview of Ensemble methods;
Detailed discussion of Random Forests;
Detailed discussion of RuleFit procedure;
Module Prerequisite
ST3007 - Multivariate Analysis and Applied Forecasting
Assessment Detail
This module will be examined in a 3 hour examination in Trinity term.
Students will be required to carry out a project employing the above techniques on a set of data using R. The project will consist of a series of mini projects over the term and will account for 40% of the total mark with an exam accounting for the remaining 60%.