Machine Learning & AI for Business (CMCE30003)

Undergraduate level 3Points: 12.5Not available in 2025

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Overview

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Machine Learning is an integral tool in a business analyst’s arsenal and plays a critical role in predicting a range of outcomes, including consumer behaviour, future performance and other organisational outcomes of interest. In real‐time, data collection and data wrangling are the important steps in deploying machine learning models.

This subject covers the application of a range of supervised and unsupervised learning techniques from machine learning in a range of semi‐ or non‐structured decision problems in a data rich environment including senior team decision support, decision optimisation across firm boundaries (e.g., supply change and supplier coordination), customer facing algorithms to support product selection and other decisions.  The techniques covered will include decision trees, regression trees, neural networks and clustering methods. Ethical concerns associated with the analysis of business problems using the methods taught in the subject will also be integrated into the subject.  

Intended learning outcomes

On successful completion of this subject, students should be able to:

  • Understand the fundamentals of AI and ML and their applications in business contexts;
  •  Apply basic programming skills in Python and utilise data libraries to generate business solutions through AI and ML;
  • Application of supervised learning algorithms including decision trees and regression trees to business problems.;
  • Application of unsupervised learning algorithms such as association rules and clustering methods, to business problems;
  • Application of neural network models and deep learning in marketing in business predictive modelling; and
  • Understanding the ethical concerns, such as AI bias, in using AI/ML in business and how to mitigate these concerns. 

Generic skills

  • High level of development: problem solving; statistical reasoning; computer programming; interpretation of the output from algorithms; written communication; data processing and management.
  • Moderate level of development: ethical considerations; application of theory to practice. 

Last updated: 4 March 2025