Machine Learning for Economics
Module title | Machine Learning for Economics |
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Module code | BEE3066 |
Academic year | 2024/5 |
Credits | 15 |
Module staff | Dr Pradeep Kumar (Convenor) |
Duration: Term | 1 | 2 | 3 |
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Duration: Weeks | 11 |
Number students taking module (anticipated) | 40 |
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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
- 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.
Learning activities and teaching methods (given in hours of study time)
Scheduled Learning and Teaching Activities | Guided independent study | Placement / study abroad |
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27 | 123 |
Details of learning activities and teaching methods
Category | Hours of study time | Description |
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Schedule learning and teaching | 22 | Lectures |
Schedule learning and teaching | 5 | Tutorials |
Formative assessment
Form of assessment | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
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In Class Exercises / Homework | Weekly in Lectures and Fortnightly in tutorials | 1-6 | Verbal/ ELE |
Summative assessment (% of credit)
Coursework | Written exams | Practical exams |
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100 | 0 | 0 |
Details of summative assessment
Form of assessment | % of credit | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
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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 |
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Homework Assignment | Homework Assignment 30% | 1-6 | August/September reassessment period |
Term Project | Term Project 70% | 1-6 | August/September reassessment period |
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.
Credit value | 15 |
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Module ECTS | 7.5 |
Module pre-requisites | BEE2031 or BEE2041 |
NQF level (module) | 6 |
Available as distance learning? | No |
Origin date | 11/03/2019 |
Last revision date | 11/03/2019 |