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,
Sem 2 2024,
Sem 1 2025

COSC3003

RMIT University Vietnam

Postgraduate

175H Computing Technologies

Face-to-Face

 Viet1 2024,
Viet1 2025

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 Courses

Successful completion of the following course/s:

Note: it is a condition of enrolment at RMIT that you accept responsibility for ensuring that you have completed the prerequisite/s and agree to concurrently enrol in co-requisite courses before enrolling in a course.

For information go to RMIT Course Requisites webpage.

 

If you have completed prior studies at RMIT or another institution that developed the skills and knowledge covered in the above course/s you may be eligible to apply for credit transfer.

Alternatively, if you have prior relevant work experience that developed the skills and knowledge covered in the above course/s you may be eligible for recognition of prior learning.

Please follow the link for further information on how to apply for credit for prior study or experience.


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

Program Learning Outcomes

This course contributes to the program learning outcomes for the following program(s):

MC271 – Master of Artificial Intelligence

PLO 4    Communication
PLO 5    Team Work
PLO 6    Responsibility
PLO 7    Research and Scholarship

For more information on the program learning outcomes for your program, please see the program guide.


Upon successful completion of this course, you will be able to:

  1. Use research principles and apply appropriate methods to analyse, theorise and justify conclusions about new situations in AI professional practice and/or research. 
  2. Plan and execute a substantial research-based project, capstone experience and/or piece of scholarship. 
  3. Apply appropriate AI techniques/tools/methodologies and reflect critically on theory and professional practice. 
  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. 
  5. 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. 

 

Assessment Tasks 

Assessment Task 1: Project plan
Weighting 15%
This assessment task supports CLOs 1, 2, 5 

Assessment Task 2: Final oral/video presentation 
Weighting 10% 
This assessment supports CLO 4  

Assessment Task 3: Final written report
Weighting 40% 
This assessment supports CLOs 1, 2, 3, 4, 5

Assessment Task 4: Presentation and team performance
Weighting: 35%
This assessment supports CLOs 1, 2, 3, 4, 5

If you have a long-term medical condition and/or disability it may be possible to negotiate to vary aspects of the learning or assessment methods. You can contact the program coordinator or Equitable Learning Services if you would like to find out more.