Course Title: Artificial Intelligence Postgraduate Project
Part A: Course Overview
Course Title: Artificial Intelligence Postgraduate Project
Credit Points: 24.00
Terms
Course Code |
Campus |
Career |
School |
Learning Mode |
Teaching Period(s) |
COSC2777 |
City Campus |
Postgraduate |
171H School of Science |
Face-to-Face |
Sem 2 2021 |
COSC2777 |
City Campus |
Postgraduate |
175H Computing Technologies |
Face-to-Face |
Sem 1 2022, Sem 2 2022, Sem 1 2023, Sem 2 2023, Sem 1 2024 |
COSC3003 |
RMIT University Vietnam |
Postgraduate |
175H Computing Technologies |
Face-to-Face |
Viet1 2024 |
Course Coordinator: Ke Deng
Course Coordinator Phone: -
Course Coordinator Email: ke.deng@rmit.edu.au
Course Coordinator Location: -
Course Coordinator Availability: By appointment, by email
Pre-requisite Courses and Assumed Knowledge and Capabilities
Enforced Pre-requisite: COSC1125 Artificial Intelligence
Course Description
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:
Communication (PLO4)
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.
Responsibility (PLO6)
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.
Upon successful completion of this course, you will be able to:
- Use research principles and apply appropriate methods to analyse, theorise and justify conclusions about new situations in AI professional practice and/or research.
- Plan and execute a substantial research-based project, capstone experience and/or piece of scholarship.
- Apply appropriate AI techniques/tools/methodologies and reflect critically on theory and professional practice.
- 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.
- Work effectively in a team environment to develop a complex software system.
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.
Assessment Tasks
Early Assessment Task: Specification of AI project scope, project plan and deliverables
Weighting 15%
This assessment task supports CLOs 1, 2, 5
Final oral and/or video presentation of project outcomes:
Weighting 10%
This assessment supports CLO 4
Final written report on project, including self-reflection and team performance/contribution:
Weighting 40%
This assessment supports CLOs 1, 2, 3, 4, 5
Presentation, communication and self-management tasks/performance
Weighting: 35%
This task is ongoing and includes the team presentation of the project results, communication with the project sponsor and self-management during the project. You will be required to submit a copy of the work log, git commit logs, meeting agendas/minutes for this assessment, as well as meet regularly with the course manager.
This assessment supports CLOs 1, 2, 3, 4, 5