Machine Learning for Economics
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.
Full module specification
|Module title:||Machine Learning for Economics|
|Duration of module:||
Duration (weeks) - term 1: |
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.
ILO: Module-specific skills
- 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
- 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
- 5. exemplify quantitative analysis and logical thinking.
- 6. demonstrate programming skills.
Learning activities and teaching methods (given in hours of study time)
|Scheduled Learning and Teaching Activities||Guided independent study||Placement / study abroad|
Details of learning activities and teaching methods
|Category||Hours of study time||Description|
|Schedule learning and teaching||22||Lectures|
|Schedule learning and teaching||5||Tutorials|
|Form of assessment||Size of the assessment (eg length / duration)||ILOs assessed||Feedback method|
|In Class Exercises / Homework||Weekly in Lectures and Fortnightly in tutorials||1-6||Verbal/ ELE|
Summative assessment (% of credit)
|Coursework||Written exams||Practical exams|
Details of summative assessment
|Form of assessment||% of credit||Size of the assessment (eg length / duration)||ILOs assessed||Feedback method|
|Homework Assignment||30||1 problem set with 10 questions||1-6||Verbal/ELE|
|Term Project||70||3000 words||1-6||Verbal/ELE|
Details of re-assessment (where required by referral or deferral)
|Original form of assessment||Form of re-assessment||ILOs re-assessed||Timescale for re-assessment|
|Homework Assignment||Homework Assignment 30%||1-6||August/September reassessment period|
|Term Project||Term Project 70%||1-6||August/September reassessment period|
- Introduction to machine learning
- Linear regression
- Classification models
- Resampling methods
- Linear model Selection and regularization
- Tree based methods
- Support vector machines
- Unsupervised learning
Note: The syllabus plan is tentative.
Indicative learning resources - 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.
Module has an active ELE page?
Last revision date