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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.  

Full module specification

Module title:Machine Learning for Economics
Module code:BEE3066
Module level:3
Academic year:2023/4
Module lecturers:
  • Dr Pradeep Kumar - Convenor
Module credit:15
ECTS value:



BEE2031 or BEE2041

Duration of module: Duration (weeks) - term 1:


Module aims

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 ActivitiesGuided independent studyPlacement / study abroad

Details of learning activities and teaching methods

CategoryHours of study timeDescription
Schedule learning and teaching22Lectures
Schedule learning and teaching5Tutorials

Formative assessment

Form of assessmentSize of the assessment (eg length / duration)ILOs assessedFeedback method
In Class Exercises / HomeworkWeekly in Lectures and Fortnightly in tutorials1-6Verbal/ ELE

Summative assessment (% of credit)

CourseworkWritten examsPractical exams

Details of summative assessment

Form of assessment% of creditSize of the assessment (eg length / duration)ILOs assessedFeedback method
Homework Assignment301 problem set with 10 questions1-6Verbal/ELE
Term Project703000 words1-6Verbal/ELE

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

Original form of assessmentForm of re-assessmentILOs re-assessedTimescale for re-assessment
Homework AssignmentHomework Assignment 30%1-6August/September reassessment period
Term ProjectTerm Project 70%1-6August/September reassessment period

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.

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.

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