Course Title: Artificial Intelligence Postgraduate Project
Part A: Course Overview
Course Title: Artificial Intelligence Postgraduate Project
Credit Points: 24.00
Course Coordinator: Julie Porteous
Course Coordinator Phone: +61 03 9925 3575
Course Coordinator Email: firstname.lastname@example.org
Course Coordinator Location: 014.08.007E
Course Coordinator Availability: By appointment, by email
Pre-requisite Courses and Assumed Knowledge and Capabilities
You should have successfully completed the course The AI Professional [COSC2778].
Alternatively, if you have not successfully completed the course The AI Professional [COSC2778] then you must enrol in it simultaneously.
This capstone course is designed to provide you with hands on practical experience of all aspects of developing an AI project.
The emphasis is on understanding and integrating all the skills and knowledge that you have acquired from your earlier courses on the program into a solid base from which to move forward into your career as an AI professional.
This course includes a Work Integrated Learning experience in which your knowledge and skills will be applied and assessed in a real workplace context. Any or all of these aspects of a WIL experience may be simulated.
Objectives/Learning Outcomes/Capability Development
The course contributes to the program learning outcomes for MC271 – Master of Artificial Intelligence:
You will learn to communicate effectively with a variety of audiences through a range of modes and media, in particular to:
- Interpret abstract theoretical propositions, choose methodologies, justify conclusions and defend professional decisions to both IT and non-IT personnel via technical reports of professional standard and technical presentations.
Team Work (PLO5)
You will learn to work as an effective and productive team member in a range of professional and social situations, in particular to:
- Work effectively in different roles, to form, manage, and successfully produce outcomes from collaborative teams, whose members may have diverse cultural backgrounds and life circumstances, and differing levels of technical expertise.
You will be required to accept responsibility for your own learning and make informed decisions about judging and adopting appropriate behaviour in professional and social situations. This includes accepting the responsibility for independent life-long learning and a high level of accountability. Specifically, you will learn to:
- Effectively apply relevant standards, ethical considerations, and an understanding of legal and privacy issues to designing AI software, applications and IT systems;
- Reflect on experience and improve your own future practice;
- Locate and use data and information and evaluate its quality with respect to its authority and relevance.
Research and Scholarship (PLO7)
You will have technical and communication skills to design, evaluate, implement, analyse and theorise about developments that contribute to professional practice or scholarship, specifically you will have cognitive skills to:
- Demonstrate mastery of theoretical knowledge and to reflect critically on theory and professional practice or scholarship;
- Plan and execute a substantial research-based project, capstone experience and/or piece of scholarship.
On completion of this course you should be able to:
- CLO 1: Use research principles and apply appropriate methods to analyse, theorise and justify conclusions about new situations in AI professional practice and/or research.
- CLO 2: Plan and execute a substantial research-based project, capstone experience and/or piece of scholarship.
- CLO 3: Apply mastery of theoretical knowledge and reflect critically on theory and professional practice.
- CLO 4: Communicate effectively to a variety of audiences through a range of modes and media, specifically, through written technical reports and presentation of your project deliverables.
Overview of Learning Activities
This is a project-based course where you learn through meetings and informal discussions with other students, the academic supervisor and where applicable other collaborators. Your learning is in the ’doing’, where you carry out all necessary steps to successfully complete your project.
All your learning activities in this course are based on applying your AI knowledge in a process of planning and executing a substantial research-based project or industry-sponsored capstone project experience.
There are no lectures in this course, but weekly or fortnightly meetings with the supervisor(s), other students working on the related projects and where applicable industry partners or other collaborators.
Each project is different and has its own individual goals and deliverables.
Overview of Learning Resources
To achieve high levels of academic results you are expected to spend an average of 20 hours per week working on the project over 12 to 14 weeks.
You will make extensive use of computer laboratories and relevant software provided by the School. You will be able to access course information and learning materials through myRMIT and may be provided with copies of additional materials in class or via email.
Lists of relevant reference texts, resources in the library and freely accessible Internet sites will be provided.
Overview of Assessment
Note: This course has no hurdle requirements.
You will be assessed based on the project deliverables, where you will apply masters level AI knowledge and skills.
The project deliverables include both documentation and a working product or a prototype or a research outcome that meets the sponsor’s requirements. The documentation includes project charter, technical solution design and any other hand-over documents/manuals requested by the industry sponsor.
Provided that you work as a team to deliver this project, your assessment will be based on your contribution in all aspects of the project, starting from requirements gathering, analysis, taking initiative to come up with solutions and development of the agreed solution and contributing to the team success. Not only limited to these, effectively responding to sponsor and project manager’s feedback will also be a key factor in the assessment.
Early Assessment Task:
Specification of AI project scope and deliverables
This assessment task supports CLOs 1,2
Final report and presentation of capstone project
This assessment supports CLOs 1,2,3,4