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.
Campus offering
No unit offerings are currently available for this unit.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
Evaluate the suitability of various machine learni...
Learning Outcome 02
Analyse the ethical, social, and practical conside...
Learning Outcome 03
Apply machine learning techniques to solve busines...
Learning Outcome 04
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
30%
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)
30%
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
40%
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.