University of Technology Sydney

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
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

Recognition of prior learning for this course is generally not applicable for students who have undertaken other postgraduate study, except as outlined below.

Students who articulate from the Graduate Diploma of Artificial Intelligence (C06147) are eligible for 48 credit points of recognition of prior learning.

Students who articulate from the Graduate Certificate in Information Technology (C11142) are eligible for up to 24 credit points of recognition of prior learning, depending on which options were chosen in the Graduate Certificate. Students are advised to study a Data Analytics stream within this Graduate Certificates if their intention is to seek recognition of prior learning in this course.

The substitution of subjects with an alternative/advanced postgraduate subject in a similar field of study may be granted on a case-by-case basis. Subject substitution and recognition of prior learning are subject to faculty approval.

Recognition of prior learning (including subject exemption and subject substitution) for the following subjects may be considered:

32144 Technology Research Preparation

49016 Technology and Innovation Management

32513 Advanced Data Analytics Algorithms

32146 Data Visualisation and Visual Analytics

32567 Business Intelligence for Decision Support

42904 Cloud Computing and Software as a Service

32130 Fundamentals of Data Analytics

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).

Other information

Further information is available from:

UTS Student Centre
telephone 1300 ask UTS (1300 275 887) or +61 2 9514 1222
Ask UTS