Unit rationale, description and aim

Machine Learning (ML) is revolutionizing business applications by enabling data-driven decision-making, automating complex processes, and uncovering insights from vast amounts of data. With the increasing availability of big data, advancements in technology, and the growing variety of commercial applications, ML has become an essential tool for businesses to remain competitive and innovative.

This unit introduces students to the fundamentals of machine learning and its transformative impact on business. It aims to equip students with a foundational understanding of ML concepts, techniques, tools, and applications in real-world business contexts. The focus is on practical, non-technical approaches to ML that demonstrate how it can be applied to solve business challenges such as customer segmentation, sales forecasting, fraud detection, and operational optimization.

By the end of this unit, students will have the skills to critically evaluate ML applications, understand their limitations, and identify opportunities to leverage ML effectively within a business environment.

2026 10

Campus offering

No unit offerings are currently available for this unit.

Prerequisites

Nil

Learning outcomes

To successfully complete this unit you will be able to demonstrate you have achieved the learning outcomes (LO) detailed in the below table.

Each outcome is informed by a number of graduate capabilities (GC) to ensure your work in this, and every unit, is part of a larger goal of graduating from ACU with the attributes of insight, empathy, imagination and impact.

Explore the graduate capabilities.

Describe key concepts, techniques, and machine lea...

Learning Outcome 01

Describe key concepts, techniques, and machine learning tools in business contexts.
Relevant Graduate Capabilities: GC1, GC9

Evaluate the suitability of various machine learni...

Learning Outcome 02

Evaluate the suitability of various machine learning models for addressing business challenges.
Relevant Graduate Capabilities: GC2, GC7

Analyse the ethical, social, and practical conside...

Learning Outcome 03

Analyse the ethical, social, and practical considerations of machine learning in business applications
Relevant Graduate Capabilities: GC6, GC7

Apply machine learning techniques to solve busines...

Learning Outcome 04

Apply machine learning techniques to solve business problems.
Relevant Graduate Capabilities: GC2, GC8

Content

Topics will include:

·        Introduction to machine learning

·        Types of machine learning (supervised, unsupervised, reinforcement)

·        Data requirements for machine learning in business applications

·        Data preprocessing and basic feature selection

·        Prediction and classification techniques

·        Clustering algorithms for revealing patterns and extracting valuable insights

·        Application of machine learning in business

·        Case studies in machine learning for business

·        Identification of suitable machine learning models for solving business problems

·        Ethical, social, and practical considerations

·        Future trends and innovation in machine learning for business

Assessment strategy and rationale

The assessments are designed to progressively develop students' technical and analytical skills in applying machine learning to business contexts. They include practical, problem-solving, and case-based tasks that simulate real-world challenges, ensuring students can translate their knowledge into actionable insights.

Assessment one involves weekly lab exercises where students implement machine learning algorithms using Python-based tools to solve business-related problems. These exercises provide hands-on experience with foundational coding techniques, allowing students to develop proficiency in AI-driven solutions without requiring extensive prior programming knowledge.

The second assessment is an individual business case study that requires students to analyze a real-world scenario, apply appropriate machine learning methods through coding, and justify their choices based on data-driven insights.

The final assessment is a group project where students collaboratively design and present a machine learning-driven solution to a business problem. This task integrates technical, ethical, and practical considerations while fostering teamwork, communication, and coding proficiency.

To pass this unit, students must achieve an aggregate mark of at least 50%. Assessments are marked using rubrics designed to measure the achievement of learning outcomes, with a final grade awarded based on overall performance.


Overview of assessments

Assessment Task 1: Weekly Lab Exercises Assessm...

Assessment Task 1: Weekly Lab Exercises

Assessment 1 includes weekly exercises to develop foundational skills in applying machine learning techniques to business problems. (6% each submission - Weeks 2, 4, 6, 8, 10)

Submission Type: Individual

Artefact: Lab files and reports

Weighting

30%

Learning Outcomes LO2, LO4
Graduate Capabilities GC2, GC3, GC8

Assessment Task 2: Business Case Study Analysis ...

Assessment Task 2: Business Case Study Analysis

Assessment 2 involves the analysis of a real-world business scenario, identifying suitable machine learning techniques and tools that can ethically address the problem.

Submission Type: Individual

Artefact: Written report (1500 words)

Weighting

30%

Learning Outcomes LO1, LO2, LO3
Graduate Capabilities GC2, GC3, GC8, GC10, GC11

Assessment Task 3: Project on Machine Learning So...

Assessment Task 3: Project on Machine Learning Solutions

Assessment 3 involves a group project where students propose a machine learning-driven solution to a specific business problem, considering ethical and practical factors.

Student should also implement a prototype of the proposed solution using programming or low-code/no-code software tools to address the specified business problem. Student also need to present their work and answer the questions of the lecturer in charge who takes the role of business partner/client during the presentation.

Submission Type: Group

Artefact: Report (500 words) + Program files and 7-10 minutes in-class presentation

Weighting

40%

Learning Outcomes LO1, LO2, LO3, LO4
Graduate Capabilities GC1, GC2, GC6, GC7, GC8, GC9

Learning and teaching strategy and rationale

Students should anticipate undertaking 150 hours of study for this unit over a twelve-week semester or equivalent study period, including class attendance, readings, online forum participation and assessment preparation.

This unit may be offered in “Attendance” or “Online” mode to cater for the learning needs and preferences of a range of participants.

Attendance Mode

Students will require face-to-face attendance in blocks of time determined by the school. Students will have face-to-face interactions with lecturer(s) to further their achievement of the learning outcomes. This unit is structured with required upfront preparation before workshops. The online learning platforms used in this unit provide multiple forms of preparatory and practice opportunities for students to prepare and revise.

Online Mode

This unit utilises an active learning approach whereby students will engage in e-module activities, readings and reflections, and opportunities to collaborate with peers in an online environment. This can involve, but is not limited to, online workshops, online discussion forums, chat rooms, guided reading, and webinars. Pre-recorded lectures will be incorporated within the online learning environment and e-modules. In addition, electronic readings will be provided to guide students’ reading and extend other aspects of online learning

Representative texts and references

Representative texts and references

Hull, J. C. (2021). Machine learning in business: An introduction to the world of data science (Third Edition).

Hudgeon, D., & Nichol, R. (2019). Machine learning for business: using Amazon SageMaker and Jupyter (First Edition). Simon and Schuster.

Albright, S., C., Winston, W., L. (2024). Business Analytics: Data Analysis & Decision Making (8th Edition). Cengage Learning.

Tan, P., Steinbach, M., Karpatne, A., Kumar, V. (2019). Introduction to Data Mining. (2nd ed.). Pearson Education Limited.

Shmueli, G., Bruce, P. C., Deokar, K. R., & Patel, N. R. (2023). Machine learning for business analytics: Concepts, techniques, and applications with analytic solver data mining. John Wiley & Sons.

IBM. (2021). IBM Cognos Analytics - Reporting User Guide (Version 11.1.0). Retrieved January 19, 2025, from https://www.ibm.com/docs/en/SSEP7J_11.1.0/com.ibm.swg.ba.cognos.ug_cr_rptstd.doc/ug_cr_rptstd.pdf

McGuirk, M. (2023). Performing web analytics with Google Analytics 4: a platform review. Journal of Marketing Analytics. 11. 1-15. 10.1057/s41270-023-00244-4.

Turban, E., Sharda, R., Delen, D., & King, D. (2019). Business Intelligence, Analytics, and Data Science: A Managerial Perspective (4th ed.). Pearson.

Santa Clara University. (2023). Applications of machine learning and AI in business. Retrieved January 19, 2025, from https://onlinedegrees.scu.edu/media/blog/applications-of-machine-learning-and-ai-in-business

Oklahoma State University. (2024). Business applications of artificial intelligence and machine learning. Retrieved January 19, 2025, from https://open.ocolearnok.org/aibusinessapplications

Deckler, G., Powell, B., & Gordon, L. (2022). Mastering Microsoft Power BI: Expert techniques to create interactive insights for effective data analytics and business intelligence. Packt Publishing Ltd.

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