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

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.

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. 

• 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. 

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.

• 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. 

Formal Contact Methods

Minimum Contact hours: 20 

Delivery format: Lecture and Workshops 

• 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.

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

• 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.

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

50% Exam

50% Group Coursework

• 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. 

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/  

Informal Contact Method

• Office Hours

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