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Curricular information is subject to change
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
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
Student Effort Type | Hours |
---|---|
Lectures | 24 |
Computer Aided Lab | 11 |
Specified Learning Activities | 25 |
Autonomous Student Learning | 60 |
Total | 120 |
A working knowledge of statistical methods including regression analysis. Familiarity with the R software for statistical computing and data programming.
Description | Timing | Component Scale | % of Final Grade | ||
---|---|---|---|---|---|
Continuous Assessment: Homework assignments, code-based exercises, data analysis tasks | Varies over the Trimester | n/a | Other | No | 30 |
Examination: End of trimester written exam | 2 hour End of Trimester Exam | No | Other | No | 70 |
Resit In | Terminal Exam |
---|---|
Autumn | Yes - 2 Hour |
• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment
Not yet recorded.
Name | Role |
---|---|
Mr Brian Buckley | Tutor |
Iuliia Promskaia | Tutor |