Module overview
Linked modules
Pre-requisites: STAT6114 or STAT6103
Aims and Objectives
Learning Outcomes
Subject Specific Intellectual and Research Skills
Having successfully completed this module you will be able to:
- Choose, compare and use appropriate machine learning techniques to address specific prediction/classification problems;
- Contrast the statistical modelling and machine learning approaches for the analysis of data;
- Assess the uncertainty associated to a given machine learning application using appropriate statistical measures;
Knowledge and Understanding
Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:
- A broad set of machine learning techniques and their use in practice.
Transferable and Generic Skills
Having successfully completed this module you will be able to:
- Communicate the results of machine learning applications to specialized and non-specialized audiences.
Syllabus
Learning and Teaching
Teaching and learning methods
Type | Hours |
---|---|
Independent Study | 70 |
Teaching | 30 |
Total study time | 100 |
Resources & Reading list
Journal Articles
Breiman, L. (2001). Statistical modeling: The two cultures.. Statistical science, 16(3), pp. 199-231.
Textbooks
Friedman, J., Hastie, T. and Tibshirani, R. (2017). The elements of statistical learning.. Springer.
James, G., Witten, D., Hastie, T. and Tibshirani, R. (2013). An introduction to statistical learning with applications in R.. Springer.
Assessment
Assessment strategy
The course will be assessed by a written report representing 100% of the marks.Summative
This is how we’ll formally assess what you have learned in this module.
Method | Percentage contribution |
---|---|
Project report | 100% |
Referral
This is how we’ll assess you if you don’t meet the criteria to pass this module.
Method | Percentage contribution |
---|---|
Project report | 100% |
Repeat Information
Repeat type: Internal & External