Master of Artificial Intelligence – VU Brisbane

Predictive Analytics
Unit code: BCO6008 | Credits: 12

Artificial Intelligence and Machine Learning for Business
Unit code: BCO7007 | Credits: 12

Data Analysis for Business
Unit code: BEO6000 | Credits: 12

AI Assisted Project Management
Unit code: NIT5000 | Credits: 12

Programming Language and Pseudocode
Unit code: NIT5001 | Credits: 12

Database Analysis and Design
Unit code: NIT5130 | Credits: 12

Advanced Object Oriented Programming
Unit code: NIT5150 | Credits: 12

Applied Natural Language Processing
Unit code: NIT6003 | Credits: 12

Neural Network and Deep Learning
Unit code: NIT6004 | Credits: 12

Generative AI Model Applications Development
Unit code: NIT6008 | Credits: 12

Intelligent Decision Making
Unit code: NIT6009 | Credits: 12

Introduction to Research
Unit code: NIT6130 | Credits: 12

Advanced Project
Unit code: NIT6150 | Credits: 12

Data Warehousing and Mining
Unit code: NIT6160 | Credits: 12

IT Research Project 1
Unit code: NIT6001 | Credits: 12

IT Research Project 2
Unit code: NIT6002 | Credits: 12

On successful completion of this course, students will be able to:

1. Critically review and theorise foundational and recent developments in artificial intelligence, including machine learning, deep learning, neural networks, natural language processing, generative AI, predictive analytics and intelligent decision-making.
2. Design and implement robust AI solutions and scalable data-driven applications with advanced problem-solving skills that address complex real-world problems individually and as a part of team.
3. Analyse structured and unstructured data using appropriate analytical, mining, language-processing, and predictive techniques to generate defensible insights for professional and business decision-making.
4. Contextualise and evaluate human-centred AI applications that respond to user, stakeholder, and organisational needs, and that augment human capability, productivity and employability in contemporary workplaces.
5. Critique AI design, development, and deployment decisions with reference to ethics, bias, privacy, security, transparency, explainability, sustainability, Aboriginal and Torres Strait Islander data sovereignty, Indigenous data governance and broader societal impact.
6. Translate AI concepts, methodologies, model outputs, and project outcomes to specialist and non-specialist audiences in collaborative professional contexts.
7. Propose and validate substantial research-based or industry-oriented AI project by applying appropriate research methods, project management and professional judgement to a complex real-world problem.
8. Critically evaluate AI systems, data practices and governance decisions through Aboriginal and Torres Strait Islander data and perspectives.

You must provide evidence that you have completed an approved English language test, and achieved the result as described below.

• IELTS (Academic Module): overall score 6.5 (no band less than 6.0)
• TOEFL internet-based test: overall score 79 (minimum section scores: listening 19, speaking 19, reading 18, writing 22)
• Pearson Test of English (PTE): overall score 58-64 (no section score less than 50)
• University of Cambridge – Advanced (CAE): total score of 58
• VU English – University Preparation UPU (Undergraduate): Achieved
• English Language School in Sydney (ELSIS) – EAP 2 (advanced): A grade.

Recognition of prior English language studies

If you've completed a qualification where English was the primary language of instruction, you may be eligible for an English language exemption. Check our guide to English language requirements to find out if you qualify.

Admission criteria

Completion of an Australian Bachelor degree (or equivalent) in a similar discipline
OR
Completion of an Australian Graduate Diploma (or equivalent) in a similar discipline
PLUS
IELTS (or equivalent): Overall score of 6.5 (with no band less than 6.0 in Listening, Reading, Writing and Speaking)

Additional information

Students who hold an AQF Level 7 (or equivalent) Bachelor Degree in Computer Science or a closely related discipline may apply for up to four units of credit (NIT5000, NIT5001, NIT5130 and/or BEO6000), with credit granted only where prior study demonstrates equivalence to the specific unit(s) for which credit is sought.

Find out if you meet the minimum academic (international qualification) requirements to be eligible to apply for this course by reviewing the academic entry requirements listed by country.

Applicants must be aged 18 or over to be eligible to apply for this course.