Master of Applied Artificial Intelligence
2019 Deakin University Handbook
| Year | 2019 course information |
|---|---|
| Award granted | Master of Applied Artificial Intelligence |
| Campus | Offered at Waurn Ponds (Geelong) |
| Cloud Campus | Yes |
| Duration | Depending on your professional experience and previous qualifications, your course will be:
|
| CRICOS course code | 0100305 Waurn Ponds (Geelong) |
| Deakin course code | S736 |
| Approval status | This course is approved by the University under the Higher Education Standards Framework |
| Australian Qualifications Framework (AQF) recognition | The award conferred upon completion is recognised in the Australian Qualifications Framework at Level 9 |
New course commencing Trimester 1 2020 | |
Course sub-headings
- Course overview
- Indicative student workload
- Career opportunities
- Participation requirements
- Mandatory student checks
- Alternative exits
- Fees and charges
- Course Learning Outcomes
- Course rules
- Course structure
- Work experience
- Other learning experiences
Course overview
Artificial intelligence is driving digital disruption, with new technology helping redefine many industries. Many companies are looking to take advantage of recent advances in artificial intelligence, which is creating a large demand for skilled professionals.
Deakin’s suite of courses in Applied Artificial Intelligence will equip you with specialist knowledge and skills necessary to design and develop advanced solutions using artificial intelligence.
The Master of Applied Artificial Intelligence will develop your understanding of the state of the art in artificial intelligence, deep learning, reinforcement learning, and the application of these algorithms in computer vision and speech processing. You will explore the complexities of introducing AI solutions in a human context, both from a conceptual and ethical perspective as well as from an engineering perspective.
As an artificial intelligence specialist, you will work alongside software engineers, data scientists, application developers and business analysts, applying your specialist knowledge to ensure artificial intelligence is appropriately integrated into software solutions from a technical and human perspective.
You will gain hands-on experience in the development of software solutions and the use and development of artificial intelligence. Our world-leading research in artificial intelligence feed directly into our classrooms, meaning that you’ll be learning at the cutting edge of industry expectations and capabilities.
As a graduate you will be well-equipped to work on design, development, and operation of software solutions involving artificial intelligence.
Units in the course may include assessment hurdle requirements.
Indicative student workload
You can expect to participate in a range of teaching activities each week. This could include classes, seminars, practicals and online interaction. You can refer to the individual unit details in the course structure for more information. You will also need to study and complete assessment tasks in your own time.
Career opportunities
Graduates will have the specialist knowledge needed to operate as Data Scientists, AI Technology Software Engineers, AI Product Managers, AI Ethicist and grow into roles such as AI Architect.
Participation requirements
Elective units may be selected that include compulsory placements, work-based training, community-based learning or collaborative research training arrangements.
Reasonable adjustments to participation and other course requirements will be made for students with a disability. Click here for more information.
Mandatory student checks
Any unit which contains work integrated learning, a community placement or interaction with the community may require a police check, Working with Children Check or other check.
Alternative exits
| Graduate Certificate of Artificial Intelligence (S536) | |
| Graduate Certificate of Information Technology (S578) | |
| Graduate Diploma of Artificial Intelligence (S636) |
Fees and charges
Fees and charges vary depending on your course, your fee category and the year you started. To find out about the fees and charges that apply to you, visit www.deakin.edu.au/fees.
Course Learning Outcomes
| Deakin Graduate Learning Outcomes | Course Learning Outcomes |
| Discipline-specific knowledge and capabilities | Develop an advanced and integrated knowledge of the technologies of artificial intelligence, including deep learning and reinforcement learning, with detailed knowledge of the application of AI algorithms across a range of domains and applications including computer vision and speech processing. Design, develop and implement software solutions that incorporate novel applications of artificial intelligence. Apply advanced knowledge of artificial intelligence to the research and evaluation of AI solutions and provision of specialist advice. Design artificial intelligence solutions that incorporate safe ethical decision making. |
| Communication | Prepare a range of technical and user-oriented documentation using adequate structure, terminology and context to address technical and non-technical audiences. Convey information and instructions in a clear, concise and coherent manner using appropriate oral communication techniques and skills for a broad range of audiences. Imagine, conceive, and represent ideas using IT conventions, modelling languages, and standards to reflect on complex artificial intelligence ideas and processes in an effective manner. Apply interpersonal skills to lead, proactively assist, contribute to ideas, respect opinions and value contribution made by others when working collaboratively with a wide range of stakeholders. |
| Digital literacy | Identify, select and use a range of digital technologies and tools to generate, manage and share digital resources associated with advanced artificial intelligence concepts and solutions. Independently and systematically locate information, evaluate its reliability, and use the information for design, problem solving and research purposes. Recommend and use appropriate practices and processes to ensure the security, integrity, safety and availability of digital resources. |
| Critical thinking | In assessing complex artificial intelligence scenarios, critically evaluate arguments, hypothesis, systems and proposals to identify basic statements. In assessing complex artificial intelligence scenarios, locate ambiguity and vagueness in arguments, requirements, and proposals to determine if ideas are reasonable, and identify information that may be contradictory, omitted, or not collected. In assessing complex artificial intelligence scenarios, apply judgement in evaluating ideas, associated reasoning, and available evidence to arrive at conclusions that are valid. |
| Problem solving | Apply expert, specialised technical skills, knowledge and techniques to identify and define complex problems utilising advanced artificial intelligence in a variety of contexts. Apply expert, specialised technical skills and knowledge in modelling methods and processes to understand problems, handle abstraction and design novel artificial intelligence solutions. Apply expert, specialised technical skills and knowledge to develop innovative and creative approaches and/or solutions in planning, designing, managing, evaluating and executing complex artificial intelligence projects. Integrate knowledge of social, safety, legal and cultural aspects to solve problems in complex and contradictory situations. |
| Self-management | Evaluate own knowledge and skills with relation to wider artificial intelligence community and use frameworks of reflection to define and progress professional goals. Recognise the need, and engage in, independent learning for continual development pf specialist knowledge and skills in artificial intelligence as a computing professional. Demonstrate the ability to accept responsibility for objectives, and work under broad direction, engaging in the feedback process independently to ensure outcomes are achieved. |
| Teamwork | Contribute specialist knowledge and skills of artificial intelligence when working within a team, demonstrating high levels of responsibility and accountability. Engage consistently and professionally in groupware to contribute expert knowledge and skills of artificial intelligence to achieve shared team objectives and outcomes. Apply strategies to lead and support positive group dynamics, manage conflict and to function effectively as a team member. |
| Global citizenship | Apply professional ethics, responsibilities, and norms of professional computing practice. Demonstrate awareness of regulation and ethical implications of acquisition, use, disclosure and eventual disposal of information. Engage with global trends and research with concern for societal, health, safety, legal, and cultural issues to effectively manage responsibilities relevant to artificial intelligence in practice. |
Approved by Faculty Board 14 March 2019
Course rules
To complete the Master of Applied Artificial Intelligence, you will complete 8, 12 or 16 credit points, depending on your prior experience.
The course is structured in three parts:
- Part A. Fundamental Information Technology Studies (4 credit points),
- Part B. Introductory Artificial Intelligence Studies (4 credit points), and
- Part C. Mastery Applied Artificial Intelligence Studies (8 credit points).
Depending upon prior qualifications and/or experience, you may receive credit for Parts A and B.
The course comprises a total of 16 credit points, which must include the following:
- 14 credit points of core units
- 2 credit points of Level 7 SIT elective units
- completion of STP050 Academic Integrity (0-credit point compulsory unit)
Course structure
Core
Mandatory unit for all entry levels
| STP050 | Academic Integrity (0 credit points) |
Part A: Fundamental Information Technology Studies
| SIT771 | Object-Oriented Development |
| SIT772 | Database and Information Retrieval |
| SIT773 | Software Requirements Analysis and Modelling |
| SIT774 | Web Technologies and Development |
Part B: Introductory Artificial Intelligence Studies
| SIT720 | Machine Learning |
| SIT787 | Mathematics for Artificial Intelligence |
| SIT719 | Security and Privacy Issues in Analytics |
Plus one elective unit
Part C: Mastery Applied Artificial Intelligence Studies
| SIT744 | Practical Machine Learning for Data Science |
| SIT796 | Reinforcement Learning ^ |
| SIT799 | Human Aligned Artificial Intelligence |
| SIT789 | Applications of Computer Vision and Speech Processing |
| SIT788 | Engineering AI Solutions |
| SIT764 | Project Analysis and Design ~ |
| SIT782 | Project Delivery ~ |
Plus one elective
~ Note: Students are expected to undertake SIT764 and SIT782 in consecutive trimesters. Students should seek advice from the unit chair if they are unable to complete SIT764 and SIT782 consecutively.
^ Available from 2021
Course structure
Elective
Plus two (2) level 7 SIT-coded elective units.
Work experience
You will have an opportunity to undertake a placement as part of your course.
Course duration - additional information
Course duration may be affected by delays in completing course requirements, such as accessing or completing work placements.
Other learning experiences
You may choose to use one of your elective units to undertake an internship or participate in an overseas study tour to enhance your global awareness and experience.