Learning Outcomes:
On completion of this module, students should have acquired the following skills:
- Have an understanding of the theory regarding all the statistical learning methods introduced.
- Being able to use the different techniques according to the context and the purpose of analysis.
- Being able to evaluate the performance of the statistical learning methods introduced.
- Use the statistical software R to implement these methods and being able to interpret the relevant output.
Indicative Module Content:
Unsupervised learning:
- Association rule analysis
- Clustering
Supervised learning:
- Logistic regression for classification
- Classification trees
- Ensemble methods
- Support vector machines
- Evaluation of classifiers, model selection, and tuning