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This unit information may be updated and amended immediately prior to semester. To ensure you have the correct outline, please check it again at the beginning of semester.
This unit introduces students to the application of machine learning (ML) and artificial intelligence (AI) in different business contexts. Students will learn how modern organisations use ML models for data analysis to support strategic planning, operational excellence, and business decision-making. Applications covered include areas such as sales forecasting, customer retention, fraud detection, and customer segmentation. The unit explores both supervised and unsupervised learning methods, model evaluation, and the use of data visualisation in a business environment. Students will work with business case studies and real datasets to select, develop, and evaluate appropriate ML models. Special focus is given to interpreting model outcomes for business stakeholders and understanding the ethical, fairness, and explainability issues that arise when applying ML and AI in business. By the end of the unit, students will be able to use machine learning techniques to analyse business data, generate actionable recommendations, support strategic and operational objectives, and critically assess the implications of their analyses in practical business settings.
In this unit, students will be developing the following capabilities:
On-campus attendance at scheduled classes is expected.
All learning experiences are delivered online and attendance at scheduled virtual classes is expected.
This is a Learning Unit. Learning Units engage students in regular learning activities to develop their knowledge, skills, and capabilities. The learning activities provide each student with feedback to support their development, and create evidence for each student’s progress towards achieving the learning outcomes of the course.
To meet the minimum requirements for this Learning Unit, you will actively engage in specified learning activities and produce a curated portfolio of work that demonstrates your knowledge, skills, and developmental progress toward the course learning outcomes. Further details are available in the unit Canvas site.
GS2 GRADING SCHEMA 2 Used for Undifferentiated Pass/Fail units inc. practical units or work-integrated learning
For the purposes of considering a request for Reasonable Adjustments under the Disability Standards for Education (Commonwealth 2005), inherent requirements for this subject are articulated in the Unit Description, Learning Outcomes and Assessment Requirements of this entry. The University is dedicated to provide support to those with special requirements. Further details on the support for students with disabilities or medical conditions can be found at the Access and Inclusion website.
The University is committed to creating an academic environment in which learning with integrity means engaging honestly, responsibly and ethically with the curriculum. Engaging in academic misconduct undermines this commitment, impedes the development of authentic knowledge and skills, and prevents meaningful learning. Academic integrity is therefore essential to the learning process and to the value of the qualifications awarded by the University.
Academic Integrity in a Learning Unit includes:
Your teaching staff will provide feedback if they have concerns that you are not acting with integrity in your learning. However, it is your responsibility to ensure that you are completing your work ethically.
In some circumstances, Students may apply for an extension in accordance with ECU policy and procedure - for more information visit https://askus2.ecu.edu.au/s/article/000001386.
Students may apply for Special Consideration in respect of a final unit grade, where their achievement was affected by Exceptional Circumstances as set out in ECU policy and procedure - for more information visit https://askus2.ecu.edu.au/s/article/000003318.
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