Unit convenor and teaching staff |
Unit convenor and teaching staff
Professor
Olivera Marjanovic
Contact via email
MQBS - Building EA4, Level 5, Office 515
Mon 11-12pm (weeks 1-13) or by email appointment
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Credit points |
Credit points
10
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Prerequisites |
Prerequisites
COMP2200
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Corequisites |
Corequisites
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Co-badged status |
Co-badged status
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Unit description |
Unit description
This unit looks at practical applications of AI systems in a business context, including how AI systems could be deployed, integrated with other business systems, maintained in the longer term, and used in ethical and responsible ways to create business and societal value. The unit takes a business rather than a technical perspective and focuses on developing AI-related business skills and capabilities which will remain relevant in the world of fast-changing AI technology. The topics will cover the Socio-technical view of AI; Business frameworks for AI; AI-enabled Business models; AI data & analytics core; different aspects of AI-enabled Business value creation, Principles and practices of Responsible AI as well as Future of work, business and humanity in AI era. The unit also introduces foundations of AI technologies suitable for business and other non-technical users and managers of AI. |
Information about important academic dates including deadlines for withdrawing from units are available at https://www.mq.edu.au/study/calendar-of-dates
On successful completion of this unit, you will be able to:
Unless a Special Consideration request has been submitted and approved, a 5% penalty (of the total possible mark) will be applied each day a written assessment is not submitted, up until the 7th day (including weekends). After the 7th day, a grade of ‘0’ will be awarded even if the assessment is submitted. Submission time for all written assessments is set at 11.55pm. A1(one) -hour grace period is provided to students who experience a technical concern.
For any late submissions of time-sensitive tasks, such as scheduled tests/exams, performance assessments/presentations, and/or scheduled practical assessments/labs, students need to submit an application for Special Consideration.
IMPORTANT: In-term quizzes are held in lectures. One out of the three quizzes is optional. If one quiz is missed for any reason, that one will be counted the optional one and no make-up quiz will be offered. If two out of the three in-term quizzes are missed and the special considerations are granted for both, only one make-up quiz will be offered. The second missed quiz will be counted as optional.
Name | Weighting | Hurdle | Due |
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Weekly Tutorial Participation | 10% | No | Ongoing (Weeks 2-13) |
Team Project | 40% | No | 01/06/2025 |
AI Industry Case Study | 30% | No | 13/04/2025 |
In-term quizzes | 20% | No | Weeks 5, 8 and 12 (24/3, 28/4 and 26/5) during lectures |
Assessment Type 1: Participatory task
Indicative Time on Task 2: 0 hours
Due: Ongoing (Weeks 2-13)
Weighting: 10%
Students will be required to actively contribute to weekly tutorials in different ways that encourage and value different learning styles and needs. Attendance does not count as contribution.
Assessment Type 1: Project
Indicative Time on Task 2: 40 hours
Due: 01/06/2025
Weighting: 40%
This assignment consists of two components: Group project (30 marks) and individual reflections on the project (10 marks). Students will be required to complete a group project related to business applications of AI in an organisational setting, taking a business perspective. Additionally, each team member will be required to complete an individual reflection on the project.
Assessment Type 1: Case study/analysis
Indicative Time on Task 2: 34 hours
Due: 13/04/2025
Weighting: 30%
Students will be required to analyse one or more AI case studies using the concepts and frameworks covered in class.
Assessment Type 1: Quiz/Test
Indicative Time on Task 2: 11 hours
Due: Weeks 5, 8 and 12 (24/3, 28/4 and 26/5) during lectures
Weighting: 20%
Students will be required to complete two or more short quizzes designed to test the AI fundamentals covered in weekly classes.
1 If you need help with your assignment, please contact:
2 Indicative time-on-task is an estimate of the time required for completion of the assessment task and is subject to individual variation
The unit is comprised of 13 x weekly classes (2h-hour lecture + 1h-tutorials), held on-campus in weeks 1 to 13. The unit is not designed for remote learning. Weekly in-person attendace is expected in both lectures and tutorials.
The unit does not have a set textbook, due to the fast changing nature of AI developments. Weekly readings will be posted on iLearn.
Macquarie University policies and procedures are accessible from Policy Central (https://policies.mq.edu.au). Students should be aware of the following policies in particular with regard to Learning and Teaching:
Students seeking more policy resources can visit Student Policies (https://students.mq.edu.au/support/study/policies). It is your one-stop-shop for the key policies you need to know about throughout your undergraduate student journey.
To find other policies relating to Teaching and Learning, visit Policy Central (https://policies.mq.edu.au) and use the search tool.
Macquarie University students have a responsibility to be familiar with the Student Code of Conduct: https://students.mq.edu.au/admin/other-resources/student-conduct
Results published on platform other than eStudent, (eg. iLearn, Coursera etc.) or released directly by your Unit Convenor, are not confirmed as they are subject to final approval by the University. Once approved, final results will be sent to your student email address and will be made available in eStudent. For more information visit connect.mq.edu.au or if you are a Global MBA student contact globalmba.support@mq.edu.au
At Macquarie, we believe academic integrity – honesty, respect, trust, responsibility, fairness and courage – is at the core of learning, teaching and research. We recognise that meeting the expectations required to complete your assessments can be challenging. So, we offer you a range of resources and services to help you reach your potential, including free online writing and maths support, academic skills development and wellbeing consultations.
Macquarie University provides a range of support services for students. For details, visit http://students.mq.edu.au/support/
The Writing Centre provides resources to develop your English language proficiency, academic writing, and communication skills.
The Library provides online and face to face support to help you find and use relevant information resources.
Macquarie University offers a range of Student Support Services including:
Got a question? Ask us via the Service Connect Portal, or contact Service Connect.
For help with University computer systems and technology, visit http://www.mq.edu.au/about_us/offices_and_units/information_technology/help/.
When using the University's IT, you must adhere to the Acceptable Use of IT Resources Policy. The policy applies to all who connect to the MQ network including students.
Unit information based on version 2025.03 of the Handbook