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MN50752: Data mining & machine learning

[Page last updated: 05 August 2021]

Academic Year: 2021/2
Owning Department/School: School of Management
Credits: 10 [equivalent to 20 CATS credits]
Notional Study Hours: 200
Level: Masters UG & PG (FHEQ level 7)
Semester 2
Assessment Summary: CW 80%, EX 20%
Assessment Detail:
  • Coursework (CW 80%)
  • Examination (EX 20%)
Supplementary Assessment:
Like-for-like reassessment (where allowed by programme regulations)
Aims: This unit will enable you to build on the skills and knowledge from unit 'Databases & Business Intelligence' and will teach you how to discover patterns in data (e.g. customer profiles of a retailer) using algorithms, as well as how to apply machine learning methods to pattern recognition problems.

Learning Outcomes:
* Choose appropriate algorithms to detect previously unknown rules and patterns within data and infer their business implications [CILO:K1, K3]
* Measure the accuracy and precision of the rules and patterns detected [CILO:I1]
* Model business challenges as data mining and machine learning models [CILO:I3, P4]
* Apply ethical principles in the collection, conversion and analysis of data [CILO:I4, K5]
* Use state-of-the-art data mining software [CILO:P2].

Skills: See LO Section.

Content: Topics covered include Clustering , Pattern Recognition, and Classification Methods.

Programme availability:

MN50752 is Compulsory on the following programmes:

School of Management


  • This unit catalogue is applicable for the 2021/22 academic year only. Students continuing their studies into 2022/23 and beyond should not assume that this unit will be available in future years in the format displayed here for 2021/22.
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