Machine Learning for Economics

Code

ECON4061

School

Economics

Level

4

Credits

15

Semesters

Autumn UK

Summary

The module will introduce students to Machine Learning and the analysis of large datasets ("big data") in economics. Large datasets have become common in economic analysis, especially because many production and consumption activities leave digital footprints. Machine learning algorithms provide useful tools for analysing such data, especially, for prediction, classification, and clustering of such data. The module provides basic knowledge and practice in implementing Machine Learning algorithms that are used in the recent literature. The module gives an introduction to those techniques, applies these to real-world datasets, and presents examples from the current economic research of the use of the techniques.

Target Students

Available for students on the MSc Economics and Data Science and MSc Financial and Computational Mathematics degrees. Also available for PGR students in the School of Economics.

Classes

This module is delivered through a combination of lectures and computer classes.

Assessment

• 25% Coursework: Group essay and presentation
• 75% Exam (2-hour): Exam

Assessed by end of autumn semester

Educational Aims

Learning Outcomes

On completion of this module students should be able to demonstrate:
A3 An understanding of advanced quantitative methods
A4 An advanced knowledge of specialisms in economics, including the current state of research in that field

B1 Model the essential features of complex economic systems
B2 Use analysis, deduction and induction to solve economic problems

C1 Understand principles of research design and strategy
C2 Find and access economic data and evidence
C3 Apply appropriate quantitative methods (mathematical, statistical, graphical) to data and evidence

D1 Communicate effectively and clearly in written and/or oral formats
D2 Use appropriate IT packages effectively

Conveners

• Dr Marit Hinnosaar