Introduction to Machine Learning

Module Aims: This module aims to introduce students to some foundational ideas in machine learning, while familiarising them with a set of canonical methods and algorithms

Module Learning Outcomes: By the end of the module, students should be able to:

1. Describe modelling assumptions, algorithms and analyses using the terminology of machine learning
2. Identify and apply appropriate machine-learning methods  to solve a range of inferential and prediction problems and critically compare the stability of results obtained using standard approaches
3. Recognise contexts in which algorithms for inference and prediction derived from flexible high dimensional models can offer advantages over classical statistical methods
4. Appreciate that flexible modelling can only lead to useful out-of-sample prediction when it is possible to impose valid structural assumptions about the data-generating process
5. Identify useful objective function penalisations corresponding to a variety of such structural assumptions
6. Interpret the output of machine learning algorithms in the context of the underlying modelling assumptions.

Pre-requisites: Advanced Biostatistics for HDS module. Specifically, familiarity with parametric regression models, methods for model comparison/choice, regularisation/penalisation, dimension reduction techniques, latent structures and cluster analysis.

Teaching Strategy: Lectures and computer practicals. Some preliminary reading may be required.

Assessment: Practical analysis with assessed report. The students will receive one dataset, and a set of questions focusing on two standard ML methods. 40% of the module grade will be assigned to each ML method; 20% of the module grade will be assigned to an open-ended discussion.

 

Module Length and Dates: 4 days