Machine Learning and Forecasting - BUSN9040

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Module delivery information

Location Term Level1 Credits (ECTS)2 Current Convenor3 2022 to 2023
Spring Term 7 15 (7.5) Shaomin Wu checkmark-circle


In this module, students will learn about the fundamentals of machine learning and forecasting techniques and gain hands-on experience with analysing and solving a variety of problems encountered in business and management.

Three indicative areas of the module could include:
- Machine learning: The introduction of modern machine learning techniques used in business data analysis, including both supervised learning (e.g. regression, classification, and artificial neural networks) and unsupervised learning (e.g. association rule discovery and cluster analysis).

- Forecasting: Students will learn about various forecasting methods, including exponential smoothing methods and the Box-Jenkins method (i.e. the ARIMA model and variants).

- Data analysis report writing. Students will systematically carry out a data analysis project and write a data analysis report.

The data analysis packages such as R, SPSS, and Weka may be used in this module.


Contact hours

Total contact hours: 36
Private study hours: 114
Total study hours: 150

Method of assessment

Main assessment methods
VLE Test: 20%
Individual Assignment (1500 words): 30%
Data Analysis Report (up to 2500 words): 50%

Reassessment method:
100% coursework.

Indicative reading

Students will also be required to read articles from academic journals like Machine Learning, Journal of Machine Learning Research, Journal of Forecasting, International Journal of Forecasting.

Box, G.E.P., Jenkins, G.M., Reinsel, G.C., Ljung, G.M. (2015) Time Series Analysis: Forecasting and Control, 5th Edn. Hoboken: Wiley. (ISBN: 978-1118674918)

James, G., Witten, D., Hastie, T., Tibshirani, R. (2013) An Introduction to Statistical Learning with Applications in R. New York: Springer. (ISBN 978-1461471370)

Hyndman, R.J., Athanasopoulos, G. (2018) Forecasting: Principles and Practice. OTexts. (ISBN 978-0987507112)

Witten, I.H., Eibe, F. (2011) Data Mining: Practical Machine Learning Tools and Techniques, 3rd Edition. San Francisco: Morgan Kaufmann. (ISBN: 978-0123748560)

See the library reading list for this module (Canterbury)

See the library reading list for this module (Medway)

Learning outcomes

The intended subject specific learning outcomes
On successfully completing the module students will be able to:
- demonstrate advanced knowledge of the types of data analysis problems that can be appropriately dealt with using machine learning and forecasting techniques.
- understand and critically discuss research issues within the area of machine learning and forecasting.
- successfully develop machine learning and forecasting models and apply them to real-world problems.

The intended generic learning outcomes
On successfully completing the module students will be able to:
- work with complex issues both systematically, critically and creatively
- demonstrate self-direction and originality in tackling and solving problems through research design, data collection, preparation, analysis, synthesis, and reporting
- demonstrate effective use of different forms of communication techniques to present complex ideas and arguments


  1. Credit level 7. Undergraduate or postgraduate masters level module.
  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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