MC-AINTL v.1 Master of Artificial Intelligence
This course is pending registration for International Onshore Students on Student Visas. Please contact the Curtin International Office to determine availability for International students.
Course Overview
Master Degrees (Coursework) prepare students to apply advanced knowledge for professional practice, scholarship and further learning corresponding to AQF level 9 qualifications.
This course provides a coverage of key aspects of artificial intelligence with latest advances in the field. It teaches students both the theoretical and practical aspects of artificial intelligence with an emphasis on the skills sought out by the industry. Fundamental knowledge that underpins the areas of artificial intelligence and hands-on experience solving real-world problems using latest AI technologies are reinforced throughout the course. Graduates from this course will have high-level knowledge of artificial intelligence as well as advanced analytical and problem-solving skills and will be able to demonstrate them by completing the project unit.
Professional Recognition
Professional recognition by the Australian Computing Society (ACS) is currently being sought for this course.
Career Opportunities
Graduates of this course will be able to work in a variety of roles in artificial intelligence, such as Data Mining Analyst, Data Scientist, Artificial Intelligence Specialist/Engineer, Business Intelligence (BI) Developer and Machine Learning Researchers.
Additional Course Expenses
Students may be expected to purchase a number of textbooks and other essential study materials.
Course Entry and Completion Details
Applicants for a Master Degree (Coursework) are required to meet University academic and English language entry standards; details are provided at http://study.curtin.edu.au/. Subject to the duration of the course applicants usually require a Bachelor Degree or equivalent (and may require relevant work experience), Bachelor Honours Degree, Graduate Certificate or Graduate Diploma. All Curtin courses have compulsory and other core capabilities that are essential for demonstrating the achievement of course learning outcomes and graduation. Students who are unable to meet or demonstrate those requirements, now or in later stages during their studies, may seek reasonable adjustments by the University wherever possible to facilitate alternative ways of achieving those requirements. If reasonable adjustments cannot be accommodated, Curtin will discuss study options to find an alternative course of study or an exit degree. Any specific course entry and completion requirements must also be met.
A 3-year recognised Bachelor degree in Information Technology or Computational Science or Engineering with a minimum of 2 years relevant work experience or a 4-year degree in Information Technology or Computational Science or Engineering.
Credit for Recognised Learning
Applications for credit towards a course are assessed on an individual basis. Credit reduces the amount of learning required to complete the course and may be granted for formal education qualifications, non-formal learning from non-award programs of study and informal learning through work experiences. Further information can be found at http://futurestudents.curtin.edu.au/non-school-leavers/rpl.cfm
Pathway to Further Study
Graduates may qualify for entry to Doctoral degrees. For further details, see the University website http://curtin.edu.au
Course Organisation
Master Degrees (Coursework) contain a series of units in a specialised area of study which may include compulsory (core), optional or elective units to cater for student preferences. They may also contain a range of majors/streams for students to choose from.
Course Learning Outcomes
A graduate of this course can:
1. demonstrate specialised knowledge in different areas of machine learning.
2. provide innovative, creative and entrepreneurial solutions underpinning science and analytical methods in artificial intelligence to address real world applications.
3. communicate effectively with digital competence solutions that involve expertise in artificial intelligence topics to experts and non-technical audiences.
4. develop leading edge technology and expert skills ensuring compliance with international and professional standards such as IEEE/ACM/ACS/ACSC/ISO.
5. create artificial intelligence solutions that are underpinned by ethics, sustainability and social responsibility.
6. demonstrate initiative, leadership when working independently and collaboratively using problem solving and decision-making skills.
Duration and Availability
The course has a duration of 1 year full time or 2 years part-time.
Location and delivery Mode
| Year | Location | Period | All* | Internal | Partially Online Internal^ | External | Fully Online# |
|---|---|---|---|---|---|---|---|
| 2021 | Bentley Perth Campus | Semester 2 | Y | ||||
| 2022 | Bentley Perth Campus | Semester 1 | Y | ||||
| 2022 | Bentley Perth Campus | Semester 2 | Y |
The information displayed above refers to study periods and locations where the course is available for first time entry. Students are normally only offered or admitted to a course once.
* The course itself may not be available either solely internally or externally but individual units may be offered in either or both of those modes. Prospective students should contact the Course Coordinator for further information.
^ Course and associated units are offered in this mode permitting International Onshore student enrolment.
# Course and associated units are offered in this online only mode and DO NOT permit International Onshore student enrolment.
| Course Structure | Hrs/Wk | Credit | |||
|---|---|---|---|---|---|
| Year 1 Semester 1 | |||||
| COMP6011 | v.1 | Advanced Artificial Intelligence Research Topics | 3.0 | 25.0 | |
| COMP6013 | v.1 | Explainable Approaches to Machine Learning | 3.0 | 25.0 | |
| COMP6012 | v.1 | Stochastic and Connectionist Approaches in Machine Learning | 3.0 | 25.0 | |
| ENGR6008 | v.2 | Research Methods for Advanced Engineering | 4.0 | 25.0 | |
| OR | |||||
| NPSC5000 | v.2 | Science Masters Research Methodologies | 3.0 | 25.0 | |
| 100.0 | |||||
| Year 1 Semester 2 | |||||
| COMP6010 | v.1 | Planning and Handling Uncertainty in Machine Learning | 3.0 | 25.0 | |
| COMP6008 | v.1 | Reinforcement Learning | 3.0 | 25.0 | |
| COMP6009 | v.1 | Search and Logic Approaches in Machine Learning | 4.0 | 25.0 | |
| COMP6002 | v.1 | Computer Science Project | 8.0 | 25.0 | |
| 100.0 | |||||
* Students must complete ENGR6008 OR NPSC5000 before enrolling into COMP6002.
Further Information
If you need more course information, you may contact the relevant areas: For Current Students: Student Services Office, please click here for further details: http://students.curtin.edu.au/contact_offices.cfm. For Domestic Future Students: Curtin Connect Future Students, enquiries: https://future.connect.curtin.edu.au/ , Tel: +61-1300 222 888. For International Future Students: Curtin International, email: international@curtin.edu.au, Tel: +61-8-9266 7331.
Course Structure Disclaimer
Curtin University reserves the right to alter the internal composition of any course to ensure learning outcomes retain maximum relevance. Any changes to the internal composition of a course will protect the right of students to complete the course within the normal time frame and will not result in additional cost to students through a requirement to undertake additional units.
Disclaimer
Information in this publication is correct at the time of printing but may be subject to change.
In particular, the University reserves the right to change the content and/or method of assessment, to change or alter tuition fees of any unit of study, to withdraw any unit of study or program which it offers, to impose limitations on enrolment in any unit or program, and/ or to vary arrangements for any program.
This material does not purport to constitute legal or professional advice.
Curtin accepts no responsibility for and makes no representations, whether express or implied, as to the accuracy or reliability in any respect of any material in this publication.
Except to the extent mandated otherwise by legislation, Curtin University does not accept responsibility for the consequences of any reliance which may be placed on this material by any person.
Curtin will not be liable to you or to any other person for any loss or damage (including direct, consequential or economic loss or damage) however caused and whether by negligence or otherwise which may result directly or indirectly from the use of this publication.
International students
International students studying in Australia on a student visa can only study full-time and there are also specific entry requirements that must be met. As some information contained in this publication may not be applicable to international students, refer to international.curtin.edu.au for further information. Australian citizens, permanent residents and international students studying outside Australia may have the choice of full-time, part-time and external study, depending on course availability and in-country requirements.
