C04443v1 Master of Artificial Intelligence
Award(s): Master of Artificial Intelligence (MAI)CRICOS code: 108843D
Commonwealth supported place?: No
Load credit points: 96
Course EFTSL: 2
Location: City campus
Overview
Course aims
Career options
Course intended learning outcomes
Admission requirements
Inherent (essential) requirements
Recognition of prior learning
Course duration and attendance
Course structure
Course completion requirements
Course program
Articulation with UTS courses
Professional recognition
Other information
Overview
Artificial Intelligence (AI) is one of the most promising technologies, driven by big data and advancements in computing power and algorithms. It has huge potential to transform economies and uncover new societal and environmental values. This course enables students to gain fundamental and advanced knowledge in data analytics and visualisation, machine learning, deep learning, and various aspects of AI. Students have opportunities to gain expertise in specialised areas such as Computer Vision or Natural Language Processing and to solve real-world problems. Hands-on experience with real-world research and industry projects enables graduates to manage the increasing challenges of creating and maintaining AI systems. Graduates from this course become socially responsible and technically competent as AI specialists, to help current and future industry across many sectors, such as banking and finance, healthcare, agriculture, infrastructure development, or natural resource management. This course is aimed at graduates from computing science, information technology or computer engineering, who wish to learn or extend their knowledge of AI in various contexts.
The postgraduate artificial intelligence program provides IT and computing professionals with the opportunity to upskill and meet the demands of this rapidly changing field. The course covers a broad range of current and emerging areas of AI including data analytics, neural networks, deep learning, reinforcement learning and ethics in AI. The course provides specialised subjects in areas including computer vision, and natural language processing to meet the growing industry demand for AI professionals with advanced technical capabilities and understanding of the risks and opportunities in this growing field.
This course provides ideal preparation for graduates seeking careers in AI and its related domains. Students in the course engage in practical, hands-on learning, using technologies to develop algorithms in various AI approaches.
Course aims
This course aims to prepare graduates with a thorough and practical grounding in a range of contemporary AI topics, as well as providing underpinning knowledge of data analytics, machine learning and explainable AI. The course also provides preparation for conducting academic research or engaging in industry projects.
The course is designed for IT and computing professionals interested in moving into an AI and data analyst career as much as for those already working in data analysis, AI or related jobs to expand their expertise and advance their career.
Career options
Career options include AI Analyst, Machine Learning Engineer, AI Specialist, Computer Vision Engineer, Natural Language Processing Engineer.
Course intended learning outcomes
| A.1 | Master of Artificial Intelligence graduates will use specialised knowledge of Indigenous Australian contexts to critically analyse and evaluate their professional capability in designing and implementing AI solutions in industry and/or research when working with and for Indigenous peoples and communities. |
| B.1 | Master of Artificial Intelligence graduates are able to identify and respond to a broad range of organisational stakeholders on Artificial Intelligence issues, and to analyse and identify ethical, personal, organisational, policy, social and environmental impacts of Artificial Intelligence in diverse contexts. |
| C.1 | Master of Artificial Intelligence graduates are lifelong learners able to demonstrate autonomy, researching solutions and apply expert judgment in the design and evaluation of innovative Artificial Intelligence solutions to organisational, societal and global problems. |
| D.1 | Master of Artificial Intelligence graduates are able to demonstrate and apply deeper and broader specialised knowledge and research skills in Artificial Intelligence and related fields of information technology, including technical skills in designing and implementing AI solutions to fit the current and emerging requirements in industry and/or research. |
| E.1 | Master of Artificial Intelligence graduates are able to communicate expertly in a variety of ways to specialist and non-specialist audiences and collaborate across functional, hierarchical and professional boundaries, within and across organisations, in local and global contexts. |
| F.1 | Master of Artificial Intelligence graduates are able to work and thrive in the world of constant technological change by being critically self-reflective, curious, action-oriented, thoughtful and life-long learning professionals, dedicated to seeking feedback, applying expert judgement, pursuing self-development and making a positive difference in organisations and the wider society. |
Admission requirements
Applicants must have completed a UTS recognised bachelor's degree, or an equivalent or higher qualification, or submitted other evidence of general and professional qualifications that demonstrates potential to pursue graduate studies.
It is a requirement that the bachelor's degree be in information technology or a related discipline, with no more than 25 per cent of subjects failed.
The English proficiency requirement for international students or local applicants with international qualifications is: Academic IELTS: 6.5 overall with a writing score of 6.0; or TOEFL: paper based: 550-583 overall with TWE of 4.5, internet based: 79-93 overall with a writing score of 21; or AE5: Pass; or PTE: 58-64 with a writing score of 50; or C1A/C2P: 176-184 with a writing score of 169.
Eligibility for admission does not guarantee offer of a place.
International students
Visa requirement: To obtain a student visa to study in Australia, international students must enrol full time and on campus. Australian student visa regulations also require international students studying on student visas to complete the course within the standard full-time duration. Students can extend their courses only in exceptional circumstances.
Inherent (essential) requirements
Inherent (essential) requirements are academic and non-academic requirements that are essential to the successful completion of a course.
Prospective and current students should carefully read the Inherent (Essential) Requirements Statement below and consider whether they might experience challenges in successfully completing this course. This Statement should be read in conjunction with the UTS Student Rules.
Prospective or current student concerned about their ability to meet these requirements should discuss their concerns with the Academic Liaison Officer in their faculty or school and/or UTS Accessibility Service on 9514 1177 or at accessibility@uts.edu.au.
UTS will make reasonable adjustments to teaching and learning, assessment, professional experiences, course related work experience and other course activities to facilitate maximum participation by students with disabilities, carer responsibilities, and religious or cultural obligations in their courses.
For course specific information see the Faculty of Engineering & Information Technology Inherent (Essential) Requirements Statement.
Recognition of prior learning
Students may be eligible for up to a total of 24cp of subject exemptions based on Recognition of prior learning (RPL) as follows:
- Students who have completed equivalent subjects in a postgraduate program or UTS undergraduate program if the subjects previously completed are deemed by the faculty to be equivalent to subjects in the course.
- Subject exemption is applicable only to STM91715 Core stream (Artificial Intelligence).
Other/additional RPL may be granted via subject substitution to an alternative/advanced postgraduate subject in a similar field of study.
Students wishing to articulate from the Graduate Certificate in Information Technology (C11142) are advised to study the Data Analytics stream in the Graduate Certificate.
To be considered for recognition of prior learning, subjects must normally have been completed no more than ten years prior to the commencement of this course.
Course duration and attendance
The course duration is two years of full-time or four years of part-time study.
Course structure
The course totals 96 credit points of study, comprising a 30-credit-point stream of core subjects, 18 credit points of artificial intelligence options, and a 12-credit-point professional stream and 24 credit points of sub-major options followed by a capstone subject (either research or industry-based).
Where applicable, project topics should be relevant to students' professional career goals and should be an area of current research interest in their area of study.
Course completion requirements
| STM91715 Core stream (Artificial Intelligence) | 30cp | |
| CBK92147 Core options (Artificial Intelligence) | 18cp | |
| STM91716 Professional Stream | 12cp | |
| CBK92150 Sub-major Choice | 24cp | |
| STM91717 Project Stream | 12cp | |
| Total | 96cp |
Course program
The programs below show a suggested sequence of subjects for a full-time student commencing the course in either Autumn session or Spring session. The program is intended as a guide only and does not take into account such factors as recognition of prior learning, changes in attendance mode and subject availability, or satisfactory academic progress. Students should consult the Timetable Planner to confirm the availability of subjects in the current academic year.
| Computer Vision sub-major, Autumn commencing, full time | ||
| Year 1 | ||
| Autumn session | ||
| 32130 Fundamentals of Data Analytics | 6cp | |
| 32555 Fundamentals of Software Development | 6cp | |
| 32144 Technology Research Preparation | 6cp | |
| Select 6 credit points from the following: | 6cp | |
| CBK92147 Core options (Artificial Intelligence) | 18cp | |
| 42904 Cloud Computing and Software as a Service | 6cp | |
| 42913 Social and Information Network Analysis | 6cp | |
| 43023 Emerging Topics in Artificial Intelligence | 6cp | |
| 43024 Introduction to Computational Intelligence | 6cp | |
| 43025 Introduction to Quantum Computing | 6cp | |
| 49275 Neural Networks and Fuzzy Logic | 6cp | |
| Spring session | ||
| 42172 Introduction to Artificial Intelligence | 6cp | |
| 32146 Data Visualisation and Visual Analytics | 6cp | |
| 57304 The Ethics of Data and AI | 6cp | |
| Select 6 credit points from the following: | 6cp | |
| CBK92147 Core options (Artificial Intelligence) | 18cp | |
| 42049 Architecting on Amazon Web Services | 6cp | |
| 42050 SAS Predictive Business Analytics | 6cp | |
| 95563 Data Driven Storytelling Studio | 6cp | |
| 32567 Business Intelligence for Decision Support | 6cp | |
| Year 2 | ||
| Autumn session | ||
| 42028 Deep Learning and Convolutional Neural Network | 6cp | |
| 42174 Artificial Intelligence Studio | 6cp | |
| 49016 Technology and Innovation Management | 6cp | |
| Select 6 credit points from the following: | 6cp | |
| CBK92147 Core options (Artificial Intelligence) | 18cp | |
| 42904 Cloud Computing and Software as a Service | 6cp | |
| 42913 Social and Information Network Analysis | 6cp | |
| 43023 Emerging Topics in Artificial Intelligence | 6cp | |
| 43024 Introduction to Computational Intelligence | 6cp | |
| 43025 Introduction to Quantum Computing | 6cp | |
| 49275 Neural Networks and Fuzzy Logic | 6cp | |
| Spring session | ||
| 43008 Reinforcement Learning | 6cp | |
| 32513 Advanced Data Analytics Algorithms | 6cp | |
| 42177 Image Processing and Pattern Recognition | 6cp | |
| Select 6 credit points from the following: | 6cp | |
| 32933 Research Project | 6cp | |
| 32040 Industry Project | 6cp | |
| Computer Vision sub-major, Spring commencing, full time | ||
| Year 1 | ||
| Spring session | ||
| 42172 Introduction to Artificial Intelligence | 6cp | |
| 32130 Fundamentals of Data Analytics | 6cp | |
| 57304 The Ethics of Data and AI | 6cp | |
| Select 6 credit points from the following: | 6cp | |
| CBK92147 Core options (Artificial Intelligence) | 18cp | |
| 42049 Architecting on Amazon Web Services | 6cp | |
| 42050 SAS Predictive Business Analytics | 6cp | |
| 95563 Data Driven Storytelling Studio | 6cp | |
| 32567 Business Intelligence for Decision Support | 6cp | |
| Year 2 | ||
| Autumn session | ||
| 32144 Technology Research Preparation | 6cp | |
| 32555 Fundamentals of Software Development | 6cp | |
| Select 12 credit points from the following: | 12cp | |
| CBK92147 Core options (Artificial Intelligence) | 18cp | |
| 42904 Cloud Computing and Software as a Service | 6cp | |
| 42913 Social and Information Network Analysis | 6cp | |
| 43023 Emerging Topics in Artificial Intelligence | 6cp | |
| 43024 Introduction to Computational Intelligence | 6cp | |
| 43025 Introduction to Quantum Computing | 6cp | |
| 49275 Neural Networks and Fuzzy Logic | 6cp | |
| Spring session | ||
| 43008 Reinforcement Learning | 6cp | |
| 32513 Advanced Data Analytics Algorithms | 6cp | |
| 42177 Image Processing and Pattern Recognition | 6cp | |
| 32146 Data Visualisation and Visual Analytics | 6cp | |
| Year 3 | ||
| Autumn session | ||
| 42028 Deep Learning and Convolutional Neural Network | 6cp | |
| 42174 Artificial Intelligence Studio | 6cp | |
| 49016 Technology and Innovation Management | 6cp | |
| Select 6 credit points from the following: | 6cp | |
| 32933 Research Project | 6cp | |
| 32040 Industry Project | 6cp | |
| Natural Language Processing sub-major, Autumn commencing, full time | ||
| Year 1 | ||
| Autumn session | ||
| 32130 Fundamentals of Data Analytics | 6cp | |
| 32555 Fundamentals of Software Development | 6cp | |
| 32144 Technology Research Preparation | 6cp | |
| Select 6 credit points from the following: | 6cp | |
| CBK92147 Core options (Artificial Intelligence) | 18cp | |
| 42904 Cloud Computing and Software as a Service | 6cp | |
| 42913 Social and Information Network Analysis | 6cp | |
| 43023 Emerging Topics in Artificial Intelligence | 6cp | |
| 43024 Introduction to Computational Intelligence | 6cp | |
| 43025 Introduction to Quantum Computing | 6cp | |
| 49275 Neural Networks and Fuzzy Logic | 6cp | |
| Spring session | ||
| 42172 Introduction to Artificial Intelligence | 6cp | |
| 32146 Data Visualisation and Visual Analytics | 6cp | |
| 57304 The Ethics of Data and AI | 6cp | |
| Select 6 credit points from the following: | 6cp | |
| CBK92147 Core options (Artificial Intelligence) | 18cp | |
| 42049 Architecting on Amazon Web Services | 6cp | |
| 42050 SAS Predictive Business Analytics | 6cp | |
| 95563 Data Driven Storytelling Studio | 6cp | |
| 32567 Business Intelligence for Decision Support | 6cp | |
| Year 2 | ||
| Autumn session | ||
| 42850 Natural Language Processing Algorithms | 6cp | |
| 42174 Artificial Intelligence Studio | 6cp | |
| 49016 Technology and Innovation Management | 6cp | |
| Select 6 credit points from the following: | 6cp | |
| CBK92147 Core options (Artificial Intelligence) | 18cp | |
| 42904 Cloud Computing and Software as a Service | 6cp | |
| 42913 Social and Information Network Analysis | 6cp | |
| 43023 Emerging Topics in Artificial Intelligence | 6cp | |
| 43024 Introduction to Computational Intelligence | 6cp | |
| 43025 Introduction to Quantum Computing | 6cp | |
| 49275 Neural Networks and Fuzzy Logic | 6cp | |
| Spring session | ||
| 43008 Reinforcement Learning | 6cp | |
| 32513 Advanced Data Analytics Algorithms | 6cp | |
| 42173 Advanced Natural Language Processing | 6cp | |
| Select 6 credit points from the following: | 6cp | |
| 32933 Research Project | 6cp | |
| 32040 Industry Project | 6cp | |
| Natural Language Processing sub-major, Spring commencing, full time | ||
| Year 1 | ||
| Spring session | ||
| 42172 Introduction to Artificial Intelligence | 6cp | |
| 32130 Fundamentals of Data Analytics | 6cp | |
| 57304 The Ethics of Data and AI | 6cp | |
| Select 6 credit points from the following: | 6cp | |
| CBK92147 Core options (Artificial Intelligence) | 18cp | |
| 42049 Architecting on Amazon Web Services | 6cp | |
| 42050 SAS Predictive Business Analytics | 6cp | |
| 95563 Data Driven Storytelling Studio | 6cp | |
| 32567 Business Intelligence for Decision Support | 6cp | |
| Year 2 | ||
| Autumn session | ||
| 32144 Technology Research Preparation | 6cp | |
| 32555 Fundamentals of Software Development | 6cp | |
| Select 12 credit points from the following: | 12cp | |
| CBK92147 Core options (Artificial Intelligence) | 18cp | |
| 42904 Cloud Computing and Software as a Service | 6cp | |
| 42913 Social and Information Network Analysis | 6cp | |
| 43023 Emerging Topics in Artificial Intelligence | 6cp | |
| 43024 Introduction to Computational Intelligence | 6cp | |
| 43025 Introduction to Quantum Computing | 6cp | |
| 49275 Neural Networks and Fuzzy Logic | 6cp | |
| Spring session | ||
| 43008 Reinforcement Learning | 6cp | |
| 32513 Advanced Data Analytics Algorithms | 6cp | |
| 42173 Advanced Natural Language Processing | 6cp | |
| 32146 Data Visualisation and Visual Analytics | 6cp | |
| Year 3 | ||
| Autumn session | ||
| 42850 Natural Language Processing Algorithms | 6cp | |
| 42174 Artificial Intelligence Studio | 6cp | |
| 49016 Technology and Innovation Management | 6cp | |
| Select 6 credit points from the following: | 6cp | |
| 32933 Research Project | 6cp | |
| 32040 Industry Project | 6cp | |
Articulation with UTS courses
This course is part of an articulated program comprising the Graduate Diploma of Artificial Intelligence (C06147) and the Master of Artificial Intelligence (C04443).
Professional recognition
The Faculty of Engineering and Information Technology is seeking accreditation from the Australian Computer Society.
Other information
Further information is available from:
UTS Student Centre
telephone 1300 ask UTS (1300 275 887) or +61 2 9514 1222
Ask UTS