| Unit name | Machine Learning for Economics |
|---|---|
| Unit code | ECONM0011 |
| Credit points | 20 |
| Level of study | M/7 |
| Teaching block(s) |
Teaching Block 2 (weeks 13 - 24) |
| Unit director | Professor. Vincent Han |
| Open unit status | Not open |
| Units you must take before you take this one (pre-requisite units) |
Econometrics with Python |
| Units you must take alongside this one (co-requisite units) |
None |
| Units you may not take alongside this one |
None |
| School/department | School of Economics |
| Faculty | Faculty of Social Sciences and Law |
This unit is designed for MSc students to learn the theory and practice of machine learning methods. Throughout the course, we overview the most popular machine learning methods, such as lasso, random forest, support vector machine, and neural network. During lectures, we will learn the basic idea, strengths, and weaknesses of each method. During labs, we will learn how to implement each method using a computer programming tool, Python, and real data.
Why is this unit important?
The goal of this unit is to provide students with practical knowledge to conduct empirical research that involves big data. This unit will be important in deciding right machine learning techniques for a given problem and in using them appropriately. At the end of the course, students will be equipped with this knowledge that will be useful in the future when working in academia and public and private sectors.
How does this unit fit into your programme of study?
Prior to this course, students will learn basic econometric models that are designed for causal analyses, such as linear regression. The current course complements causal analyses by focusing more on prediction problems when data exhibit a large number of variables and/or a large sample size.
This unit will introduce popular supervised and unsupervised statistical learning methods with their comparative weaknesses and strengths in different settings of complex data analysis. While one of the key goals will be to use the methods for prediction and estimation, the students will also learn how one can use them for causal analysis.
As a result of this unit, student will understand how each machine learning method works and what its scope of applicability is. They will also be able to think critically about how to approach a given data set with these methods. From this training, students will be able to conduct compelling empirical research.
At the end of this course, students will be able to perform the following advanced computing tasks independently:
1. Identify strengths and weaknesses of different machine learning methods including supervised learning (classification and regression, e.g. neural networks) and unsupervised learning (clustering)
2. Use Python to analyse data for economic applications in line with theoretical economic and statistical models.
3. Mange and visualize complex datasets in Python.
4. Write advanced new functionalities for complex computational tasks, including numerical optimization, and critically assess existing implementations.
Teaching will be delivered through a combination of large and small group classes supported by online resources.
Specifically:
· A set of lectures covering each of the topics.
· Computer classes in which we go through implementation of the taught methods. We will also apply the methods to empirical examples.
Tasks which help you learn and prepare you for summative tasks (formative):
Computer classes with Python training and implementation
Tasks which count towards your unit mark (summative):
Coursework (40%): Python exercises where machine learning methods are implemented with real data, ILOs 1-4
Exam (2hr) (60%): Asking knowledge about machine learning methods (no questions about Python), ILOs 1, 4
When assessment does not go to plan
Single assessment – Exam (2hr) - assessing all ILOs
If this unit has a Resource List, you will normally find a link to it in the Blackboard area for the unit. Sometimes there will be a separate link for each weekly topic.
If you are unable to access a list through Blackboard, you can also find it via the Resource Lists homepage. Search for the list by the unit name or code (e.g. ECONM0011).
How much time the unit requires
Each credit equates to 10 hours of total student input. For example a 20 credit unit will take you 200 hours
of study to complete. Your total learning time is made up of contact time, directed learning tasks,
independent learning and assessment activity.
See the University Workload statement relating to this unit for more information.
Assessment
The Board of Examiners will consider all cases where students have failed or not completed the assessments required for credit.
The Board considers each student's outcomes across all the units which contribute to each year's programme of study. For appropriate assessments, if you have self-certificated your absence, you will normally be required to complete it the next time it runs (for assessments at the end of TB1 and TB2 this is usually in the next re-assessment period).
The Board of Examiners will take into account any exceptional circumstances and operates
within the Regulations and Code of Practice for Taught Programmes.
