• University of Exeter Business School
• Module

Machine Learning for Economics

Module description

Machine Learning is likely to have a big impact on Economics. In the era of so called ‘Big Data’, machine learning algorithms provide an extremely useful toolkit for prediction (regression), classification and clustering data. For example, machine learning algorithms can be used to find and cluster together similar consumer reviews, and lasso and ridge regression allow new kinds of econometric analysis to meaningfully analyse datasets with more variables than observations. This module introduces students to these new techniques, applying them to real datasets. Students will explore the trade-offs between expressiveness of datasets, and over-fitting, allowing them to understand how best to apply these techniques in economics.  

Module aims - intentions of the module

This module will enable students to understand, apply and interpret findings from the commonly used predictive algorithms.

A student undertaking this module should have a good grasp of intermediate econometrics and some experience with a programming language.

Intended Learning Outcomes (ILOs)

ILO: Module-specific skills

On successfully completing the module you will be able to...

• 1. illustrate the key characteristics of the popularly used machine learning algorithms.
• 2. interpret and report the findings of the machine learning methods using a programming language.

ILO: Discipline-specific skills

On successfully completing the module you will be able to...

• 3. explain the commonly used supervised and unsupervised methods in machine learning.
• 4. assess the role played by prediction problems in economics.

ILO: Personal and key skills

On successfully completing the module you will be able to...

• 5. exemplify quantitative analysis and logical thinking.
• 6. demonstrate programming skills.

Syllabus plan

1. Introduction to machine learning
2. Linear regression
3. Classification models
4. Resampling methods
5. Linear model Selection and regularization
6. Tree based methods
7. Support vector machines
8. Unsupervised learning

Note: The syllabus plan is tentative.

Learning activities and teaching methods (given in hours of study time)

Details of learning activities and teaching methods

Formative assessment

Summative assessment (% of credit)

Details of summative assessment

Details of re-assessment (where required by referral or deferral)

Indicative learning resources - Basic reading

Basic reading:

James, G., Witten, D., Hastie T. and Tibshirani, R. (2017) An Introduction to Statistical Learning (with Applications in R), Springer.

Geron, A. (2019), Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, O’Reilly. 

Rogers, S. and Girolami, M (2016), A First Course in Machine Learning, second edition, Chapman and Hall/CRC.

McCarthy, R.V., McCarthy, M.M., Ceccucci, W and Halawi, L. (2019), Applying Predictive Analytics: Finding Value in Data, Springer.

Key words search

Machine Learning