MSc AI-Driven Innovation

P89

理學碩士(人工智能驅動創新)

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Year of Entry

2026

Application Deadline

20250925T020000Z
20260531T155900Z

Mode of Study

Combined ^†

Mode of Funding

Non-government-funded

Indicative Intake Target

50

Minimum No. of Credits Required

30

Normal Study Period

Full-time: 1 year;
Part-time: 2 years;

Maximum Study Period

Full-time: 2.5 years;
Part-time/Combined mode: 5 years;

Mode of Processing

Applications are processed on a rolling basis. Review of applications will start before the deadline and continue until all places are filled. Early applications are therefore strongly encouraged.

Tuition Fee

Programme Leader

Prof LUO Jianxi

BE & MS (Tsinghua University) MS & PhD (Massachusetts Institute of Technology)

General Enquiries

3442 9321

sye.office@cityu.edu.hk

Programme Outlines

Programme Aims and Objectives
Programme Content
Entrance Requirements

Outline

Programme Aims and Objectives

The programme is designed to equip students with the essential knowledge, skills, and mindset to become leaders in technological innovation in the AI age. The rapid advancements in artificial intelligence (AI) have created unprecedented opportunities for innovation across industries. The programme places a strong emphasis on AI-driven innovation, focusing on two key dimensions:

• AI as an enabler of innovation – using AI-powered tools and methodologies to enhance the process of designing new materials, devices, products, processes, services, and systems for innovation.

• AI as an intelligent feature in innovation – integrating AI as a core intelligent function in new products, processes, services, and systems for innovation.

This programme responds to the growing global and local demand for talents who can fuse AI and innovation expertise to drive transformation across industries. By blending technological innovation and AI with systems design thinking, as well as entrepreneurial leadership, the programme aims to nurture innovation leaders, who are capable of shaping the future of industries in the fourth industrial revolution.

Programme Content

Required Core Courses (12 credit units)

Course Code	Course Title	Credit Units
SYE6012	Technological Innovation and Entrepreneurship	3
SYE6302	Design Science	3
SYE6601	Introduction to Artificial Intelligence: Concepts and Applications	3
SYE6602	AI-Driven Innovation: Seminars and Projects	3

Programme Electives (18 credit units)

Course Code	Course Title	Credit Units
SYE5006	Operations Management	3
SYE5009	Industrial Marketing Management for Engineers	3
SYE5010	Engineering Management Principles and Concepts	3
SYE6009	Project Management	3
SYE6015	Supply Chain Management	3
SYE6037	Managing Strategic Quality	3
SYE6050	Engineering Economic Analysis	3
SYE6053	Business Process Improvement and Innovation	3
SYE6102	Managerial Decision-Making Systems with Artificial Intelligence	3
SYE6103	Financial Engineering for Engineering Managers	3
SYE6105	Risk and Decision Analysis	3
SYE6106	Intelligent Manufacturing for Engineering Managers	3
SYE6110	Data Analysis and Artificial Intelligence for Systems Engineering	3
SYE6610	AI Innovation Internships	3
SYE6612	The Fourth Industrial Revolution	3
SYE6620	AI-Based Media Entrepreneurship	3
CAI6002	Venture Creation Seminar	3
SM5345	Introduction to Digital Processes: From Creative Computation to Fabrication	3
SM5354	Design Thinking and Innovation in Media	3
IS5113	AI Ethics and Regulations	3
IS5542	Generative Artificial Intelligence for Business	3
IS6423	Artificial Intelligence for Business Applications	3
IS6620	Large Language Model with Prompt Engineering for Business	3
SDSC6004	Data Analytics for Smart Cities	3
SDSC6016	Predictive Analytics and Financial Applications	3
SDSC8007	Deep Learning	3
SDSC8009	Data Mining and Knowledge Discovery	3
EE5434	Machine Learning for Signal Processing Applications	3
EE5437	Internet of Things Technologies for Future City Applications	3
EE5438	Applied Deep Learning	3
EE5606	Artificial Intelligence for Antennas in Wireless Communication	3
EE6435	Multi-Dimensional Data Modeling and its Applications	3
EE6621	Computational Physiology and Neural Systems	3
MNE6001	CAD/CAM Integration	3
MNE6002	Computer Controlled Systems	3
MNE6007	Advanced Automation Technology	3
MNE6126	Sensors for Robotics, AI and Control Systems	3
MNE6128	Advanced Machine Learning and Quantum Computation for Engineering	3
NS5007	Human and Artificial Intelligence	3
NS5009	Ethical Application of Artificial Intelligence in Biological Sciences and Healthcare	3
NS6002	Advanced Computational Neuroscience	3
BME5110	Biomedical Engineering Design	3
BME6135	Engineering Principles for Drug Delivery	3
BME6138	Robotics in Minimally Invasive Healthcare	3
BMS5010	AI in Health Science Research & Management	3
BMS5011	Wearable Technologies & Health Science Research	3
BMS8110	Genomics and Bioinformatics	3
PH5101	Health Economics and Outcomes Research	3
PH5105	Basic Biostatistics in Public Health	3
PH5106	Fundamentals of Epidemiology	3
PH6202	Infectious Disease Epidemiology	3
PH6204	Public Health Surveillance & Risk Analysis	3

Remark: These elective courses will be offered subject to the availability of resources.

Total Credit required for the MSc Programme: 30

Entrance Requirements

Applicants must be a Bachelor’s degree holder. All disciplines are welcome.

English Proficiency Requirements

Applicants whose entrance qualification is obtained from an institution where the medium of instruction is NOT English should also fulfill the following minimum English proficiency requirement:

a score of 79 (Internet-based test) in the Test of English as a Foreign Language (TOEFL)@#; or
an overall band score of 6.0 in International English Language Testing System (IELTS)@##; or
a score of 450 in the new College English Test (CET6) of Chinese Mainland or a pass in the old CET6 test; or
other equivalent qualifications.

^@TOEFL and IELTS scores are considered valid for two years. Applicants are required to provide their English test results obtained within the two years preceding the start of the University's application period. 'TOEFL iBT Home Edition', ‘TOEFL MyBest Score’, ‘IELTS Indicator’, ‘IELTS One Skill Retake’ and ‘IELTS Online’ are not acceptable.

^#Applicants are required to arrange with the Educational Testing Service (ETS) to send their TOEFL results directly to the University. The TOEFL institution code for CityUHK is 3401.

^##Applicants are required to arrange for sending their IELTS result(s) via the IELTS Results Service e-delivery to the University. Please note that applicants with an overall band score of 6.0 will be required to pass an interview for English proficiency conducted by the concerned academic unit. Interviews for other applicants may not be required.

† Combined mode: Local students taking programmes in combined mode can attend full-time (12-18 credit units per semester) or part-time (no more than 11 credit units per semester) study in different semesters without seeking approval from the University.For non-local students, they will be admitted to these programmes for either full-time or part-time studies. Non-local students must maintain the required credit load for their full-time or part-time studies and any changes will require approval from the University.