MC271 - Master of Artificial Intelligence

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Plan: MC271 - Master of Artificial Intelligence
Campus: City Campus

Program delivery and structure

Approach to learning and assessment
Work integrated learning
Program structure
Program transition plan

Approach to learning and assessment

Your learning experiences will involve a broad mix of study modes, including lectures, tutorials, practical classes, studios, project work and seminars, using face-to-face, online, intensive, and other flexible delivery mechanisms.

Assessment is designed to give you opportunities to demonstrate your capabilities. You will find that the forms of assessment used may be different for each course, depending on the course objectives and learning outcomes.

Your assessment in this program will include all or some of the following:

  • Timed Assessments: an individual form of assessment where you are asked to demonstrate your ability to explain principles and to solve problems;

  • Assignments and projects: some will require you to demonstrate an ability to work alone, while some will involve group work requiring you to be part of a team with other students;

  • Reflective journals: where you pause to consider what you have learnt and reflect on the further development of the related capability;

  • Assessed tutorials or presentations: a form of in-class test which you will be required to complete either individually or as a team:

  • Self-assessment and peer-assessment: for assessment activities such as seminars you may be asked to assess your own work, the work of your group, or the work of other groups. This is part of equipping you to become more independent in your own learning and to develop your assessment skills.

Assessments you complete will enable the teaching staff to provide you with feedback on your progress. This will enable you to improve your performance in the future.

If you have long-term medical condition, disability and/or other form of disadvantage it may be possible to negotiate to vary aspects of the learning or assessment methods. You should contact the Program Manager or Equitable Learning Services team for further information.

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Work integrated learning

RMIT is committed to providing students with an education that strongly links formal learning with workplace experience. As a student enrolled in an RMIT program you will:

  • undertake and be assessed on a structured activity that allows you to learn, apply and demonstrate your professional or vocational practice
  • interact with industry and community when undertaking this activity
  • complete an activity in a work context or situation that may include teamwork with other students from different disciplines.
  • underpin your learning with feedback from interactions and contexts distinctive to workplace experiences.

In this program, you will be doing specific courses that focus on work integrated learning (WIL). You will be assessed on professional work in a workplace setting and receive feedback from those involved in your industry. Any or all of these aspects of a WIL experience may be in a simulated workplace learning environment. 

COSC2777 Artificial Intelligence Postgraduate Project is a capstone course designed to provide you with hands-on practical experience. 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. The emphasis is on understanding and working within a corporate environment and integrating all the skills and knowledge that you have acquired from your previous courses into a solid base to progress from into your professional life. This course includes a Work Integrated Learning experience in which your knowledge and skills will be applied and assessed in a real or simulated workplace context and where feedback from industry and/ or community is integral to your experience.

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Program Structure

To graduate you must complete the following. All courses listed may not be available each semester.
 

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Year One of Program

Complete 72 credit points from the following:

Course Title

Credit Points

Course Code

Campus

Programming Fundamentals 12 COSC2531 City Campus
Discrete Mathematics 12 MATH2415 City Campus
The AI Professional 12 COSC2778 City Campus
Practical Data Science with Python 12 COSC2670 City Campus
Artificial Intelligence 12 COSC3117 City Campus
Algorithms and Analysis 12 COSC3119 City Campus
AND
Complete 24 credit points from the Program Options listed at the end of this program structure.
 
AND

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Year Two of Program

Complete 48 credit points from the following:

Course Title

Credit Points

Course Code

Campus

Intelligent Decision Making 12 COSC2780 City Campus
Programming Autonomous Robots 12 COSC2781 City Campus
Deep Learning 12 COSC2779 City Campus
Computational Machine Learning 12 COSC2793 City Campus
 
AND

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Project/Research Options

{
Project Option: Complete 24 credit points from the following:

Course Title

Credit Points

Course Code

Campus

Artificial Intelligence Postgraduate Project 24 COSC2777 City Campus
AND
Complete 24 credit points from the Program Options listed at the end of this program structure.
}
OR
Research Option: Complete 48 credit points from the following:

Course Title

Credit Points

Course Code

Campus

Computing Research and Project Preparation 12 COSC3130 City Campus
Minor Thesis/Project 36 COSC2179 City Campus
 
AND

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Program Options

Program Options List

Course Title

Credit Points

Course Code

Campus

Advanced Programming for Data Science 12 COSC2820 City Campus
Data Mining 12 COSC3125 City Campus
Agent-Oriented Programming and Design 12 COSC3123 City Campus
Evolutionary Computing 12 COSC3124 City Campus
Applied Bayesian Statistics 12 MATH2269 City Campus
Regression Analysis 12 MATH1312 City Campus
Social Media and Networks Analytics 12 COSC2671 City Campus
Games and Artificial Intelligence Techniques 12 COSC3144 City Campus
Mixed Reality 12 COSC3140 City Campus
Advanced Programming 12 COSC1295 City Campus
Cloud Computing 12 COSC2640 City Campus
Big Data Processing 12 COSC2637 City Campus
Big Data Management 12 COSC2636 City Campus
Database Systems 12 COSC3138 City Campus
iPhone Software Engineering 12 COSC3139 City Campus
Programming Internet of Things 12 COSC2755 City Campus
 

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Program transition plan

Semester 1, 2025 Transition Plan

Minor amendments have been made to your program effective semester 1, 2025. If you have completed any course that is now removed, your credit will still count towards your completion.

Details of amendment:

The following courses have a new course code with no change to course title or content:

Course Title Old Course Code New Course Code
Artificial Intelligence COSC1125 COSC3117
Algorithms and Analysis COSC1285 COSC3119
Agent-Oriented Programming and Design COSC2048 COSC3123
Evolutionary Computing COSC2033 COSC3124
Data Mining COSC2111 COSC3125
Computing Research and Project Preparation COSC2149 COSC3130
Database Systems COSC2407 COSC3138
iPhone Software Engineering COSC2472 COSC3139
Mixed Reality COSC2477 COSC3140
Games and Artificial Intelligence Techniques COSC2528 COSC3144

 

Transition Plan 2023

Amendments to the program structure of the Master of Artificial Intelligence have been made, effective Semester 1 2023. The following shows courses that will be replaced from Semester 1 2023 for program transition purposes only and not necessarily course equivalents. If you have successfully completed any of these courses prior to the commencement of Semester 1 2023 they will continue to count as courses and contribute towards the successful completion of your program. 

Year One of Program:

Prior to 2023 Credit Points Replacement Courses Credit Points
One Artificial Intelligence Option course 12 Two Program Option courses 24
One Information Technology Option course 12

Year Two of Program:

Project Option - Prior to 2023 Credit Points Replacement Courses Credit Points
One Artificial Intelligence Option course 12 Two Program Option courses 24
One Information Technology Option course 12

and

Research Option - Prior to 2023

Credit Points Replacement Courses Credit Points
COSC2389 Minor Thesis/ Project Part A 24 COSC2179 Minor Thesis / Project 36
One Artificial Intelligence Option course 12

and

Program Options List:

The Artificial Intelligence and Information Technology option lists have been replaced with one, merged Program Option List. Three courses are not included in the merged Program Option List, and have been removed from the program:

  • COSC2642 Cloud Infrastructures
  • COSC2347 Mobile Application and Development
  • INTE2401 Cloud Security

**IMPORTANT TRANSITION INFORMATION FOR 2021**

MATH2319 Machine Learning has been removed from the structure and replaced with COSC2793 Computational Machine Learning. What does this mean?

  • If you have previously completed MATH2319 Machine Learning, you will not be required to undertake COSC2793 Computational Machine Learning in the new program structure and will still receive credit for MATH2319 Machine Learning.
  • If you did not successfully complete MATH2319 Machine Learning in 2020, you will be required to undertake COSC2793 Computational Machine Learning in its place.

COSC2784 Discrete Structure in Computing has been removed from the structure and replaced with MATH2415 Discrete Mathematics. What does this mean?

  • If you have previously completed COSC2784 Discrete Structure in Computing, you will not be required to undertake MATH2415 Discrete Mathematics in the new program structure and will still receive credit for COSC2784 Discrete Structure in Computing.
  • If you did not successfully complete COSC2784 Discrete Structure in Computing in 2020, you will be required to undertake MATH2415 Discrete Mathematics in its place.
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