Curriculum
All students are required to complete a total of 30 credits, consisting of 7 required courses and 3 elective courses, for the award of this MSc degree. These courses can have different combinations of lectures, laboratory sessions, tutorials, and/or seminars. The normal duration for the MScAIBA programme is one year of full-time study. Students may extend the study period up to a maximum duration of three years, subject to the approval of the Programme Director and Head of the Department.
Programme Curriculum
| Required Courses (21 credits) | Credits |
|---|---|
| 1. CDS521: Foundation of Artificial Intelligence | 3 |
| 2. CDS522: Business Data Management | 3 |
| 3. CDS523: Principle of Data Analytics and Programming | 3 |
| 4. CDS524: Machine Learning for Business | 3 |
| 5. CDS525: Practical Application of Deep Learning | 3 |
| 6. CDS527: Big Data Analytics | 3 |
| 7. CDS529: Project for Artificial Intelligence and Business Analytics | 3 |
| Elective Courses (9 credits) | Credits |
|---|---|
| 1. ORM505: Mobile Technology and Applications in eBusiness | 3 |
| 2. ORM510: Social Media for eBusiness | 3 |
| 3. ORM511: Project Management with Software | 3 |
| 4. ORM515: Business Decision Making with Software | 3 |
| 5. CDS526: Artificial Intelligence Based Optimization | 3 |
| 6. CDS528: Blockchain | 3 |
| 7. CDS530: Healthcare Analytics | 3 |
| 8. CDS531: Marketing Analytics and Intelligence | 3 |
| 9. CDS538: Cloud Computing | 3 |
| 10. CDS539: Natural Language Processing | 3 |
| 11. CDS540: Computer Vision | 3 |
| 12. CDS542: Data Visualization | 3 |
| 13. CDS547: Introduction to Large Language Models | 3 |
| 14. CDS550 Programming with Generative Artificial Intelligence | 3 |
| 15. CDS555 Agentic AI | 3 |
| 16. SCI501: Geospatial Intelligence for Sustainable Development | 3 |
* The offering of elective courses is subject to sufficient demand and faculty availability.
Pre-entry Courses
Applicants with no or limited background in computer science or statistics will be required to complete the corresponding pre-entry courses below:
Introduction to Computing
Statistics
Progression
For progression, students are required to achieve a Cumulative G.P.A. of 2.50 or above in order to proceed to the next term. Students who cannot meet such requirement will normally be put on academic probation in the following term or discontinued.
Graduation
For graduation, students must complete 10 courses (i.e., 7 required courses and 3 elective courses) and obtain a minimum of 30 credits with a Cumulative G.P.A. of 2.67 or above.