MSc Business Analytics: Operational Research and Risk Analysis / Course details

Year of entry: 2025

Course unit details:
Machine Learning for Business

Course unit fact file
Unit code BMAN60422
Credit rating 15
Unit level FHEQ level 7 – master's degree or fourth year of an integrated master's degree
Teaching period(s) Semester 2
Available as a free choice unit? No

Overview

This course covers the fundamentals of machine learning as a core area of artificial intelligence, introduces key machine learning techniques across three main categories: supervised learning, unsupervised learning, and reinforcement learning, and explores the challenges and solutions of applying machine learning to solve real-world business problems.

Pre/co-requisites

BMAN60422 Programme Req: BMAN60422 is only available as a core unit to students on MSc Business Analytics and as an elective to students on MEng (Hons) Computer Science

BMAN60422 is available as a core unit to students on MSc Business Analytics and as an elective with permission to students on other relevant MSc programmes. 

Aims

  • Provide students with an understanding of learning as a core area of artificial intelligence and its applications in business.
  • Introduce key machine learning techniques across three main categories: supervised learning, unsupervised learning, and reinforcement learning.
  • Explore the challenges and solutions of applying machine learning to solve real-world business problems, with an emphasis on model interpretability, data quality issues, and imbalanced datasets. 

Learning outcomes

At the end of the course unit, student should be able to:

  • Understand the fundamentals of data analytics and its application to business and management decision making,
  • Understand a variety of data analysis techniques, such as data preprocessing, classification and clustering, prediction and forecasting, association rule mining & text mining, etc.,
  • Discuss how visual analytics can be used to understand big data, extract insights and identify patterns,
  • Demonstrate the ability to use specialised software tools to analyse large sets of data in real-world problems.

Syllabus

  • Introduction to Machine Learning: providing an overview of the fundamental concepts and definitions of machine learning in the context of artificial intelligence.
  • Supervised Learning: covering the basics from descriptive to predictive analytics and introducing various predictive modelling techniques, such as regressions, decision tree models, and neural networks.
  • Unsupervised Learning: exploring clustering analysis, association analysis, and collaborative filtering.
  • Reinforcement Learning: studying reinforcement learning and sequential decision-making in a stochastic environment.
  • Ensemble Learning: exploring bootstrap aggregating, boosting, stacking, and XGBoost.
  • Interpretability in Machine Learning: discussing the importance of interpretable machine learning for business applications.
  • Machine Learning for Business: challenges and solutions: addressing key challenges in machine learning for business, such as data quality issues and imbalanced datasets, while exploring their potential solutions and future advancements. 

Teaching and learning methods

Formal Contact Methods

Minimum Contact hours: 20 

Delivery format: Lecture and Workshops 

Knowledge and understanding

  • Understand the fundamentals of machine learning in the context of artificial intelligence
  • Understand and critically evaluate key machine learning techniques across the three main categories: supervised learning, unsupervised learning, and reinforcement learning.
  • Understand the importance of interpretable machine learning for business applications.

Intellectual skills

  • Frame real-world business problems through a machine learning lens and identify the appropriate modelling paradigm for a given problem.

Practical skills

  • Demonstrate the ability to use specialised software tools and programming packages to analyse large datasets and build machine learning models for real-world business problems.

Transferable skills and personal qualities

  • Develop digital and teamwork skills through case studies and coursework project.

Assessment methods

50% Exam

50% Group Coursework

Feedback methods

  • Generic feedback posted on Blackboard regarding overall examination performance. 
  • Written and/or verbal comments on assessed or non-assessed coursework. 
  • Responses to student emails and questions from a member of staff including feedback provided to a group via an online discussion forum. 

Recommended reading

Galit Shmueli,et al.; Data Mining for Business Analytics: Concepts, Techniques, and Applications - in R (e-book available from the university library) or in Python, John Wiley & Sons, 2018. 

INFORMS Analytics Magazine, http://www.analytics-magazine.org/  

Study hours

Scheduled activity hours
Lectures 20
Practical classes & workshops 10
Independent study hours
Independent study 120

Additional notes

Informal Contact Method

  • Office Hours
  • Online Learning Activities (blogs, discussions, self assessment questions)

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