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

Eligibility and requirements

Prerequisites

One of the following

Code Name Teaching period Credit Points
ECON20003 Quantitative Methods 2
Semester 1 (On Campus - Parkville)
Semester 2 (On Campus - Parkville)
 12.5
ECOM20001 Econometrics 1
Semester 1 (On Campus - Parkville)
Semester 2 (On Campus - Parkville)
 12.5
MGMT20005 Business Decision Analysis
Semester 2 (On Campus - Parkville)
 12.5
MKTG20004 Market and Business Research
Semester 1 (On Campus - Parkville)
 12.5

Corequisites

None

Non-allowed subjects

None

Inherent requirements (core participation requirements)

The University of Melbourne is committed to providing students with reasonable adjustments to assessment and participation under the Disability Standards for Education (2005), and the Assessment and Results Policy (MPF1326). Students are expected to meet the core participation requirements for their course. These can be viewed under Entry and Participation Requirements for the course outlines in the Handbook.

Further details on how to seek academic adjustments can be found on the Student Equity and Disability Support website: http://services.unimelb.edu.au/student-equity/home

Last updated: 4 March 2025

Assessment

DescriptionTimingPercentage

Weekly problem set (10 sets)

  • 500 words (equivalent)
Throughout the teaching period30%

Mid-semester test

  • 1 hours
Week 720%

Tutorial participation/class learning activities 

Throughout the teaching period10%

End-of-semester examination

  • 2 hours
Hurdle requirement: Successful completion of this subject requires a pass (50%) in the final exam		During the examination period40%

Last updated: 4 March 2025

Dates & times

Not available in 2025

Time commitment details

36 hours comprising two 1‐hour lectures and one 1‐hour tutorial per week

What do these dates mean

Visit this webpage to find out about these key dates, including how they impact on:

  • Your tuition fees, academic transcript and statements.
  • And for Commonwealth Supported students, your:
       -  Student Learning Entitlement. This applies to all students enrolled in a Commonwealth Supported Place (CSP).
     

    Subjects withdrawn after the census date (including up to the 'last day to withdraw without fail') count toward the Student Learning Entitlement.

Last updated: 4 March 2025

Further information

Last updated: 4 March 2025