+60142521561
MSc in Artificial Intelligence (ODL) - 100%online
About
APU's Master of Science in Artificial Intelligence program is designed to provide you an advantage with cutting-edge technology aligned with the Industrial Revolution 4.0. The program offers students practical experience in applying Artificial Intelligence (AI) to address real-world problems. It provides a comprehensive understanding of key areas such as Machine Learning, Robotics, and Natural Language Processing. Students will gain the skills necessary to manage and execute large-scale, complex AI projects, while ensuring the design and implementation of reliable, high-quality AI systems. The program also focuses on recognizing challenges in AI system development and proposing effective solutions. It highlights the importance of AI-driven infrastructure in giving businesses a competitive advantage and empowers students to utilize AI to overcome human limitations and solve emerging problems.
This program, available online and part time (2.5-3 years) is tailored to offer:
- Advanced skills and techniques in artificial intelligence
- Research opportunities to address significant industrial challenges using AI
- In-depth research opportunities in AI, preparing students for doctoral studies.
Key facts
| Qualification | Master's Degree |
| Qualification Subtype | Master of Science (MSc) |
| Coursework / Research | Coursework |
| Study mode | Part-time, Online |
| Duration | 3 years |
| Intakes | April, June, August, October |
| Tuition (Local students) | S$ 10,467 |
| Tuition (Foreign students) | S$ 10,467 |
Subjects
-
Computer Science, IT
Duration
3 years
Tuition fees
| Description | Local students | Foreign students |
|---|---|---|
| Tuition fee | S$ 10,467 | S$ 10,467 |
| Miscellaneous fees | Data not available | Data not available |
| Total estimated cost of attendance | S$ 10,467 | S$ 10,467 |
| Estimated cost per year | S$ 3,489 | S$ 3,489 |
Miscellanous fees explained
Local students
| Description | Amount |
|---|---|
|
Library Deposit
(refundable)
Payable for the use of library facilities and is refundable on completion of studies. |
S$ 151 |
|
Engineering Laboratory Deposit
(refundable)
Payable for the use of the Engineering laboratory facilities and is refundable on completion of studies. |
S$ 151 |
| Enrollment Fees | S$ 242 |
Foreign students
| Description | Amount |
|---|---|
|
Application & Student Visa Fees
STAGE 1 (Payable upon acceptance of offer) RM 2,650 (approx. USD 620)* is payable when you have accepted the offer. This fee includes Visa Processing Fees by Education Malaysia Global Services (EMGS) and APU/APIIT Application & Processing Fees. This also entitles you to airport assistance and transfer to the University/accommodation upon first arrival in Malaysia. Please note that this Fee is non-refundable once the offer has been accepted. STAGE 2 (Payable upon Student Pass Visa Approval) RM 1,500 (approx. USD 350)* is payable once your Student Pass is approved. This fee includes the post-arrival processing fee, EMGS medical check-up in Malaysia, Medical Insurance for a year, i-Kad (International Student Card issued by Immigration Malaysia) charged to ALL International students in Malaysia, Electronic Visa Approval Letter (eVal), and the Student Pass Fees for one-year. The i-Kad issued by Immigration Malaysia will enable students to avoid having to carry their passports around for identification purposes and will be tied to their Student Pass Validity and will be re-issued at visa renewal. Please note that this Fee is non-refundable once the Student Pass Visa Approval Letter (VAL) has been issued by EMGS. |
S$ 1,259 |
|
International Student Registration & Administrative Fees
A fee of RM 5,000 is payable as registration & administrative fee for International Students. The Fees includes Student ID Card Fee for the duration of the course. |
S$ 1,517 |
|
Library Deposit
(refundable)
Payable for the use of library facilities and is refundable on completion of studies. |
S$ 151 |
|
Engineering Laboratory Deposit
(refundable)
Payable for the use of the Engineering laboratory facilities and is refundable on completion of studies. |
S$ 151 |
| Personal Bond (refundable) | S$ 303 |
Estimated cost as reported by the institution. There may be additional administrative fees. Please contact for the latest information.
Every effort has been made to ensure that information contained in this website is correct. Changes to any aspects of the programmes may be made from time to time due to unforeseeable circumstances beyond our control and the Institution and EasyUni reserve the right to make amendments to any information contained in this website without prior notice. The Institution and EasyUni accept no liability for any loss or damage arising from any use or misuse of or reliance on any information contained in this website.
Admissions
Intakes
Entry Requirements
GENERAL REQUIREMENTS |
|
|
• Bachelor’s degree in Computing or related fields with a minimum CGPA of 2.50, or its equivalent qualification as accepted by the Senate. |
|
* Applicants without a Computing-related Bachelor’s degree must pass the pre-requisite modules to continue with the Master’s Degree.
Note: The above entry requirements may differ for specific programmes based on the latest programme standards published by Malaysian Qualifications Agency (MQA).
ENGLISH REQUIREMENTS |
|
|
INTERNATIONAL STUDENTS |
• IELTS : 6.0 |
Curriculum
This programme comprises 10 coursework modules, including 7 core modules and 3 elective modules, and a Project.
Pre-Requisite Modules (FOR NON-COMPUTING STUDENTS)
Duration: 4 Months
- Programming in Python
- Introduction to R Programming
- Fundamentals of Artificial Intelligence
Core Modules
- Artificial Intelligence
- Image Processing and Computer Vision
- Fuzzy Logic
- Applied Machine Learning
- Computational Intelligence Optimization
- Natural Language Processing
- Research Methodology in Computing and Engineering
- Project
Elective Modules (Choose 3)
- Applied Robotics
- Pattern Recognition
- Expert Systems and Knowledge Engineering
- Business Intelligence Systems
- Multivariate Methods for Data Analysis
- Deep Learning