The purpose of the module will be to introduce students to the fundamentals of machine learning, i.e. computational methods for statistical learning, prediction and decision-making using data. The basic principles of predictive modelling will be outlined, and then demonstrated using various machine learning methods and appropriate data sets.
pre-requisites: MATH2010 or STAT6123 or MATH6174
Having successfully completed this module you will be able to:
36 Lectures and/or problem classes.
| Type | Hours |
|---|---|
| Independent Study | 114 |
| Teaching | 36 |
| Total study time | 150 |
James, G., Witten, D., Hastie, T. and Tibshirani, R. (2013). An Introduction to Statistical Learning with Applications in R. Springer..
Arnold, T., Kane, M. and Lewis, B.W. (2019). A Computational Approach to Statistical Learning. CRC Press..
Hastie, T., Tibshirani, R. and Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer..
The assessment for the repeat candidates will be based completely on the final examination.
This is how we’ll formally assess what you have learned in this module.
| Method | Percentage contribution |
|---|---|
| Coursework | 50% |
| Exam | 50% |
This is how we’ll assess you if you don’t meet the criteria to pass this module.
| Method | Percentage contribution |
|---|---|
| Exam | 100% |
Repeat type: Internal & External