Machine Learning for Economists - ECON6010

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Module delivery information

Location Term Level1 Credits (ECTS)2 Current Convenor3 2024 to 2025
Spring Term 6 15 (7.5) checkmark-circle


This module introduces students into the application of machine learning techniques for the analysis of real-life economic problems. The module consists of two parts. The theoretical part teaches computational and machine learning techniques developed for economists. Here the students will develop theoretical knowledge to apply them correctly in various real-life economic problems as well as correctly and critically interpret the results of machine learning analysis. The application part of the module will demonstrate how economists apply these techniques, including causal inference, using practical examples and hands-on experience.

The module builds upon the Level 4 Programming for Artificial Intelligence (COMP3590), as well as Level 5 modules. Introduction to Econometrics (ECON5800) and Introduction to Time Series Econometrics (ECON5810)


Contact hours

Private Study: 126
Contact Hours: 24
Total: 150


Compulsory Module for: BSc Economics with Data Science
Optional to the following courses: All other Single Honours Degree Programmes in Economics

Method of assessment

Main assessment methods
ICT (20%)
Final Project (5,000 words) (80%)

Indicative reading

The University is committed to ensuring that core reading materials are in accessible electronic format in line with the Kent Inclusive Practices.

The most up to date reading list for each module can be found on the university's reading list pages.

Learning outcomes

Demonstrate confidence and flexibility in the use of industry-standard statistical software and develop understanding of programming languages commonly used in economics
Apply knowledge and understanding of machine learning and computational economics concepts used intensively in economics
Synthesise and critically compare different machine learning concepts
Demonstrate analytical skills used to formulate and consider a range of problems and issues related to machine learning
Apply numerical optimization methods to a range of machine learning concepts
Demonstrate critical understanding of statistical, graphical and numerical big data analyses in the context of economic theory and machine learning

Retrieve, review and analyse big data and information from a variety of sources
Address a problem using deductive and inductive reasoning, analyse the logic of arguments, and critically evaluate models
Apply advanced machine learning methods to support understanding of economic
Develop and apply modelling skills for industry and policy analysis using industry platforms
Communicate coherent ideas and arguments using a variety of methods
Plan work and study independently.


  1. Credit level 6. Higher level module usually taken in Stage 3 of an undergraduate degree.
  2. ECTS credits are recognised throughout the EU and allow you to transfer credit easily from one university to another.
  3. The named convenor is the convenor for the current academic session.
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