MSc AI-Driven Innovation

P89

理學碩士(人工智能驅動創新)

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Year of Entry

2027

Application Deadline

20260915T020000Z
20270131T155900Z

Mode of Study

Combined ^†

Mode of Funding

Non-government-funded

Indicative Intake Target

Minimum No. of Credits Required

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
Entrance Requirements
Programme Content

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.

MSc AIDI Poster

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.

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 fulfil the following minimum English proficiency requirement:

·  a score of 79 (for tests taken prior to 21 January 2026) or 4 (for tests taken from 21 January 2026 onwards) (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 Chinese Mainland’s College English Test (CET-6); 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. All TOEFL results must be sent directly by the Educational Testing Service (ETS) using CityUHK’s institution code 3401, while IELTS results must be sent via the IELTS Results Service e-delivery to 'City University of Hong Kong - Graduate School'.

#Applicants with an IELTS overall band score of 6.0 or a TOEFL overall score of 4 will be required to pass an interview for English proficiency conducted by the concerned academic unit. Applicants with a comparable overall score of 79 or above shown on the TOEFL report may be exempt from the interview requirement. Interviews for other applicants may not be required.

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
SYE6309	Smart City and AI Technologies	3
SYE6610	AI Innovation Internships	3
SYE6612	The Fourth Industrial Revolution	3
SYE6620	AI-Based Media Entrepreneurship	3
SYE6621	Agentic AI for Innovation	3
SYE6631	AI for Financial Services	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
DSC6004	Data Analytics for Smart Cities	3
DSC6016	Predictive Analytics and Financial Applications	3
DSC8007	Deep Learning	3
DSC8009	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

† 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.