Statistical Machine Learning

Description

Overview: By the end of this course, students are expected to understand the basic theories of machine learning and know how to construct a machine model for a real dataset using R. Students are expected to understand ethical issues regarding data processing and management. This class will run as a 10-week block beginning in week 1 of term 2, and the teaching consists of lectures, tutorials and computer labs. The majority of the course content will be delivered during the lectures. Computer labs and tutorials are particularly emphasised, where students will work to apply learned techniques to examples. Learning Outcomes: On completion of the class students should be able to: - clean data using RStudio and the tidyverse - understand missing data and the role it plays - understand ethical issues regarding data processing and management - carry out single value imputation - carry out multiple imputed chained equations in R - understand and implement artificial neural networks - understand and implement support vector machines - understand and implement tree based classification and regression techniques - understand and implement ensemble methods Assessment: The class will be assessed via a project worth 30% of the overall mark for the class. There will be a class test held under exam conditions during the April exam diet worth 70%. COURSEWORK Assessment released: 6th March 2026 Submission deadline: 2nd April 2026