MIS710 - Machine Learning in Business
Unit details
| Year | 2027 unit information |
|---|---|
| Enrolment modes | Trimester 1: Burwood (Melbourne), Online |
| Credit point(s) | 1 |
| EFTSL value | 0.125 |
| Unit chair | Trimester 1: Lemai Nguyen Trimester 2: Lemai Nguyen |
| Prerequisite | Nil |
| Corequisite | Nil |
| Incompatible with | Nil |
| Educator-facilitated (scheduled) learning activities - on-campus unit enrolment | 1 x 1.5 hour on-campus (live-streamed) lecture (recordings provided) and 1 x 1.5 hour on-campus seminar each week |
| Educator-facilitated (scheduled) learning activities - online unit enrolment | 1 x 1.5 hour recorded lecture provided and 1 x 1.5 hour online seminar (recordings provided) each week |
| Typical study commitment | Students will on average spend 150 hours over the trimester period undertaking the teaching, learning and assessment activities. |
| Supplementary information | ^GIFT City (India) offering is available to students enrolled at the GIFT City (India) campus only. |
Content
Machine Learning allows computers to learn from hidden patterns in big data to quantitatively support business decisions. In this unit, we will cover a large range of methods and algorithms that learn from big data, allowing decision makers to view previously hidden patterns and relationships and build suitable models to support business decision making.
In this unit, students will be introduced to fundamental programming concepts required by business professionals to work with machine learning concepts. This unit introduces machine learning techniques using software package Python, where the emphasis will be on solving business problems using the analysis of business data.
Learning outcomes
| ULO | These are the Unit Learning Outcomes (ULOs) for this unit. At the completion of this unit, successful students can: | Alignment to Deakin Graduate Learning Outcomes (GLOs) |
|---|---|---|
| ULO1 | Analyse and frame business challenges using machine learning concepts, techniques, and the machine learning model development lifecycle. | GLO1: Discipline-specific knowledge and capabilities GLO3: Digital Literacy |
| ULO2 | Select and apply appropriate machine learning techniques to solve business problems and evaluate the machine learning model performance. | GLO3: Digital Literacy GLO5: Problem solving |
| ULO3 | Explain the application of machine learning and interpret the outcomes to the various stakeholders. | GLO2: Communication |
Note – From 2026, Deakin commenced introducing Graduate Attributes (GAs), which define the distinctive characteristics of a Deakin graduate. You may notice some units still refer to Graduate Learning Outcomes (GLOs) during this transition period.
Assessment
| Assessment Description | Student output | Grading and weighting (% total mark for unit) | Indicative due week |
|---|---|---|---|
| Assessment 1: (Individual) Case study: Data analysis with Written Report (Business) | 2000 words | 40% | Week 6 |
| Assessment 2: Part A: (Individual) Report Part B: (Individual) Report | Part A: 2000 words Part B: 1000 words | Total 60%: Part A: 40% Part B: 20% | Week 12 |
The assessment due weeks provided may change. The Unit Chair will clarify the exact assessment requirements, including the due date, at the start of the teaching period.
Learning resource
The texts and reading list for MIS710 can be found via the University Library.
Note: Select the relevant trimester reading list. Please note that a future teaching period's reading list may not be available until a month prior to the start of that teaching period so you may wish to use the relevant trimester's prior year reading list as a guide only.
Bring Your Own Device (BYOD)
To fully engage with Deakin's learning experiences, students must be able to access and use internet-connected devices as outlined in computing requirements at Deakin.
To support student success at Deakin, we have a bring-your-own-device (BYOD) learning environment that acknowledges that students and educators bring with them the digital tools they regularly use to complete academic tasks. These tools stay with you beyond the classroom, helping you to keep learning, explore ideas more deeply, and connect with knowledge in ways that matter to you.
Students requiring a loan device should visit our Loan Laptop webpage or students requiring longer-term assistance should visit our Student Financial Assistance webpage.
Unit fee information
Fees and charges vary depending on the type of fee place you hold, your course, your commencement year, the units you choose to study and their study discipline, and your study load.
Tuition fees increase at the beginning of each calendar year and all fees quoted are in Australian dollars ($AUD). Tuition fees do not include textbooks, computer equipment or software, other equipment or costs such as mandatory checks, travel and stationery.
For further information regarding tuition fees, other fees and charges, invoice due dates, withdrawal dates, payment methods visit our Current Students website.