Machine learning is a field devoted to developing algorithms that adapt their behaviour to data, providing useful representations of the data and/or predictions. This course covers some fundamental theoretical concepts in machine learning, and common patterns for implementing methods in practice. The intended audience are those wanting the background required to begin research and development of machine learning methods.

One two-hour lecture per week for 10 weeks.

One two-hour computer lab per week for 10 weeks.

None

Assessment

The main aim of this course is to introduce methods and algorithms drawn from statistical machine learning and data science, and allow students to apply these into an array of empirical problems in economics and finance. The precise set of methods and algorithms used to illustrate and explore the main concepts will change slightly from year to year. However, the main topic headings are expected to be fairly stable.

On completion of this course, the student will be able to:

1. Structure an applied problem as a machine learning task, identifying appropriate methods.

2. Criticise and validate alternative machine learning methods for a given task.

3. Devise and motivate novel variants of machine learning methods.

4. Write accessible and useful explanations of the workings and failure modes of machine learning methods.

5. Program and refine implementations of learning algorithms, while applying them in practice.

Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.