C06147v1 Graduate Diploma in Artificial Intelligence
Award(s): Graduate Diploma in Artificial Intelligence (GradDipAI)CRICOS code: 108844C
Commonwealth supported place?: No
Load credit points: 48
Course EFTSL: 1
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 broaden their knowledge and skills in Artificial Intelligence. Graduates of this course have a strong foundation in data analytics, visualisation, machine learning, and other aspects of AI. Students gain hands-on experience with a variety of AI projects, which will equip them to engage with challenges throughout a wide range of industries that utilise AI. The course is aimed at graduates from disciplines that are different from computing science, information technology or computer engineering, and who wish to upskill and enhance their knowledge in the area of AI.
The graduate diploma artificial intelligence course 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, data visualization, machine learning and neural networks.
This course provides ideal preparation for graduates seeking careers in data analytics, AI/ML engineer and its related domains. Students in the course engage in practical and hands-on learning by using technologies to develop algorithms in various AI fields.
Course aims
This course aims to prepare graduates with a practical grounding in a range of contemporary AI topics, as well as providing underpinning knowledge of data analytics and visualisation, machine learning and explainable AI. The course is designed for IT and computing professionals interested in moving into a junior data analyst or machine leaning/AI engineer career as much as for those already working in database, AI or related jobs to expand their expertise and advance their career.
Career options
Career options include Junior Analyst- Machine learning, Software Engineer -Machine Learning, Junior AI Specialist, Junior Data Analyst, Junior Machine Learning Engineer.
Course intended learning outcomes
| A.1 | Graduate Diploma of Artificial Intelligence graduates will use knowledge of Indigenous Australian contexts to apply professional capabilities across the design and implementation of AI solutions in industry when working with and for Indigenous peoples and communities. |
| B.1 | Graduate Diploma in Artificial Intelligence graduates are able to identify a broad range of organisational stakeholders on Artificial Intelligence issues, and to identify ethical, personal, organisational, policy, social and environmental impacts of Artificial Intelligence in diverse contexts. |
| C.1 | Graduate Diploma in Artificial Intelligence graduates are lifelong learners able to demonstrate autonomy and apply expert judgment in the design and evaluation of innovative Artificial Intelligence solutions to organisational, societal and global problems. |
| D.1 | Graduate Diploma in Artificial Intelligence graduates are able to demonstrate and apply specialised knowledge in Artificial Intelligence and related fields of information technology, including technical skills in designing and implementing AI solution incorporating current and emerging technologies. |
| E.1 | Graduate Diploma in Artificial Intelligence graduates are able to communicate professionally 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 | Graduate Diploma in Artificial Intelligence graduates are able to work and thrive in the world of constant technological change by being self-reflective, curious, action-oriented, thoughtful and life-long learning professionals, dedicated to seeking feedback, applying well developed 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 one year of full-time or two years of part-time study.
Course structure
The course totals 48 credit points of study, comprising a 30-credit-point stream of core subjects, 12 credit points of artificial intelligence options, and a 6-credit-point professional option.
Course completion requirements
| STM91715 Core stream (Artificial Intelligence) | 30cp | |
| CBK92148 Core options (Artificial Intelligence) | 12cp | |
| CBK92149 Professional Choice | 6cp | |
| Total | 48cp |
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.
| Autumn commencing, full time | ||
| Year 1 | ||
| Autumn session | ||
| 32130 Fundamentals of Data Analytics | 6cp | |
| 32146 Data Visualisation and Visual Analytics | 6cp | |
| Select 6 credit points from the following: | 6cp | |
| 42904 Cloud Computing and Software as a Service | 6cp | |
| 42913 Social and Information Network Analysis | 6cp | |
| 43024 Introduction to Computational Intelligence | 6cp | |
| 43025 Introduction to Quantum Computing | 6cp | |
| 43023 Emerging Topics in Artificial Intelligence | 6cp | |
| 49275 Neural Networks and Fuzzy Logic | 6cp | |
| Select 6 credit points from the following: | 6cp | |
| 57304 The Ethics of Data and AI | 6cp | |
| 49016 Technology and Innovation Management | 6cp | |
| Spring session | ||
| 42172 Introduction to Artificial Intelligence | 6cp | |
| 32555 Fundamentals of Software Development | 6cp | |
| 32513 Advanced Data Analytics Algorithms | 6cp | |
| Select 6 credit points from the following: | 6cp | |
| 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 | |
| Spring commencing, full time | ||
| Year 1 | ||
| Spring session | ||
| 42172 Introduction to Artificial Intelligence | 6cp | |
| 32130 Fundamentals of Data Analytics | 6cp | |
| 32555 Fundamentals of Software Development | 6cp | |
| Select 6 credit points from the following: | 6cp | |
| 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 | ||
| 32146 Data Visualisation and Visual Analytics | 6cp | |
| 32513 Advanced Data Analytics Algorithms | 6cp | |
| Select 6 credit points from the following: | 6cp | |
| 42904 Cloud Computing and Software as a Service | 6cp | |
| 42913 Social and Information Network Analysis | 6cp | |
| 43024 Introduction to Computational Intelligence | 6cp | |
| 43025 Introduction to Quantum Computing | 6cp | |
| 43023 Emerging Topics in Artificial Intelligence | 6cp | |
| 49275 Neural Networks and Fuzzy Logic | 6cp | |
| Select 6 credit points from the following: | 6cp | |
| 57304 The Ethics of Data and AI | 6cp | |
| 49016 Technology and Innovation Management | 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
UTS Student Centre
telephone 1300 ask UTS (1300 275 887) or +61 2 9514 1222
Ask UTS