Module overview
Aims and Objectives
Learning Outcomes
Learning Outcomes
Having successfully completed this module you will be able to:
- demonstrate knowledge and understanding of a broad set of machine learning techniques and their applications in practice;
- understand the uncertainty associated with a given machine learning application;
- appraise the properties of machine learning for specific prediction or classification problems;
- communicate machine learning applications to non-specialized audiences.
- contrast statistical modelling and machine learning approaches to analysis and use of data;
Syllabus
Learning and Teaching
Teaching and learning methods
Type | Hours |
---|---|
Guided independent study | 34 |
Independent Study | 90 |
Online Course | 26 |
Total study time | 150 |
Resources & Reading list
Journal Articles
Breiman, L. (2001). Statistical modelling: The two cultures.. Statistical Science, pp. 16:199-231.
Bradley Efron (2020). Prediction, Estimation, and Attribution. Journal of the American Statistical Association, pp. 115:636-655.
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
Summative
This is how we’ll formally assess what you have learned in this module.
Method | Percentage contribution |
---|---|
Coursework | 100% |
Referral
This is how we’ll assess you if you don’t meet the criteria to pass this module.
Method | Percentage contribution |
---|---|
Coursework | 100% |
Repeat
An internal repeat is where you take all of your modules again, including any you passed. An external repeat is where you only re-take the modules you failed.
Method | Percentage contribution |
---|---|
Coursework | 100% |
Repeat Information
Repeat type: Internal & External