Contents of this publica�on are subject to review and change.

PolyU COMP

M a s t e r o f S c i e n c e i n

INFORMATIONTECHNOLOGY資訊科技理學碩士學位課程

ARTIFICIAL INTELLIGENCEBIG DATA ANALYTICSFINANCIAL TECHNOLOGYCYBER SECURITY & CRYPTOCURRENCYCLOUD COMPUTINGNATURAL LANGUAGE PROCESSINGSOFTWARE DEVELOPMENT & MANAGEMENT

Department of ComputingPQ806

Mong Man Wai BuildingThe Hong Kong Polytechnic University

Hung Hom KowloonHong Kong

comp.enquiry@polyu.edu.hkwww.comp.polyu.edu.hk

Stream in AI and Big Data

Stream in AI and FinTech

ABOUTTHE DEPARTMENT OF COMPUTINGThe Department of Computing (COMP) has a proud history of over 40 years. It was established in 1974 as one the pioneers offering computing education in the territory, nurturing professional talents to support the society’s advancement. Today, COMP has gained international recognition in world-class research and high quality education. It is ranked 51st - 100th in the QS World University Rankings by Subject 2018 – Computer Science & Information Systems and ranked 37th in Computer Science in the “2018 Best Global Universities Rankings” by the U.S. News and World Report. Looking into the future, COMP is determined to assert its position as the leader in interdisciplinary research and education in computing, generating worldwide impact and benefiting to mankind. COMP envisions that it will become the indispensable and influential icon in smart computing internationally.

COMP is devoted to creating a nurturing environment and excellent learning experiences for students, inspiring them to unleash their potentials into becoming future innovators. It delivers high quality research postgraduate programmes, taught master programmes and undergraduate programmes in computer science and multidisciplinary areas of information technology and enterprise computing. Its programmes integrate theories and applications, meeting the needs of students who wish to pursue their career in computing related fields.

TAUGHT POSTGRADUATE PROGRAMMEOBJECTIVEOur Master of Science in Information Technology (MScIT)* programme is a popular taught postgraduate programme that has been brought up-to-date with recent technological advancements. It is designed to nurture IT professionals and leaders for the long-term benefit of Hong Kong. The programme focuses on three important areas of information technology: Artificial Intelligence (AI), Big Data and Financial Technology (FinTech), which are in great demand by Hong Kong business, industry, government and society. It assists students to develop their technical competence and apply new knowledge, thus enhancing their competitiveness in their IT career. Graduates of this programme will be in good time and position to contribute to innovation and technology development and at the same time improve Hong Kong’s sustainability, efficiency and continuous economic development.

*This programme’s revision is subject to approval.

Promising career advancementWith the strategic planning and investment of the HKSAR Government on the Smart City initiative, Hong Kong will be moving forward with new development in AI, FinTech, Robotics, and life sciences. Besides, data-driven technology development will be considered by the HKSAR Government as one of the top priorities. Hence, our programme with AI, Big Data and FinTech technologies as the core focus, is rightfully aligned with the strategic direction of the HKSAR Government in technology development. With the increasing demand for IT expertise locally and globally, our graduates, who are well equipped with comprehensive and professional IT knowledge, will be developed to be critical thinkers, effective communicators, innovative problem solvers and socially responsible global citizens. They will embark on promising career development in many sectors.

The career opportunities for our students who complete the programme are multifold. Our graduates are expected to be well prepared for jobs such as consultants in Big Data, Cloud Computing, Artificial Intelligence and Financial Technology, or IT managers, IT project officers and software engineers.

Advanced learning facilities and joint laboratoriesPolyU has established the University Research Facility in Big Data Analytics (UBDA), the first university-wide research facility in big data analytics among universities in Hong Kong. Equipped with big data expertise in PolyU and the most advanced computing infrastructure and tools today, UBDA is expected to foster cross-disciplinary research collaborations in PolyU, establish a strong partnership with industries on big data analytics applications, and promote big data education in Hong Kong.

At COMP, we have joined forces with Monash University and CollinStar Capital to establish the first university-industry joint research laboratory on blockchain and cryptocurrency technologies in Hong Kong with multi-million-dollar industrial sponsorship. We have also established other joint laboratories and have been working closely with industry-leading companies such as IBM, Microsoft, Yonyou, Alibaba and Tencent. These joint laboratories are not only supporting research activities but also teaching and learning as well as knowledge transfer purposes. They also facilitate students to gain practical knowledge and to keep in pace with the industry development.

COMP provides other resources for students to undertake projects and dissertations on different topics. Students have the opportunity to access the Virtual Reality and Augmented Reality System in the Virtual-reality and Game Lab as well as the Big Data and Cloud Computing Platform to perform their research studies. Students are also welcome to attend all the research seminars held by the Department to understand the latest state of research in the IT field.

PROGRAMME FEATURESOur programme is a science and technology based programme tailored to nurture Mathematics, Information Systems, Engineering, or other Science graduates to become IT professionals and to enrich Computing / Computer Science graduates with advanced knowledge and skills in the important areas, especially AI, Big Data and FinTech. The programme provides the knowledge, support and guidance to students to continue lifelong learning and development.

According to Elsevier’s Scopus, PolyU was ranked the 8th in the world and the 1st in Hong Kong in field-weighted citation impact of publications on AI research from 2011 to 2015. COMP conducts advanced research contributing to the world’s fast technological growth and is dedicated to offer computing education that is in line with the development of the latest information technology.

Practical industry workshops and training sessionsOur programme will offer extra learning opportunities to enhance students’ study experience. Programming workshops will be part of the learning and teaching activities designed to help students with no or limited programming experience. We will offer workshops on various topics including AI, Blockchain and Machine Learning to strengthen students’ technical skills and hands-on experiences. Besides, we will also invite renowned speakers to deliver talks, seminars and forums, such as Cyber Security, FinTech application in banking and insurance industry, ePayment services, Initial Coin Offering to help students enrich industrial experience and raise awareness of the latest technologies development.

Wide variety of subjects that cover hot IT topicsGiven the rapid evolution of the IT industry, this programme offers a range of core and elective subjects that are in line with the fast-changing IT market needs as well as students’ demand especially in the three booming areas: Artificial Intelligence, Big Data and FinTech. Students enjoy flexible options in selecting subjects that fit their interests and career goals.

COMP frequently reviews the programme curriculum and we have recently introduced some new subjects such as “Machine Learning and Data Analysis”, “Cyber and Internet Security”, “Distributed Ledger Technology, Cryptocurrency and E-Payment”, “Computer Vision and Image Analysis”, etc.

Flexible to choose the mode of study and stream(s) This programme offers both full-time and part-time mode of study. Different study patterns including the combination of a dissertation, a project and subjects of equivalent credits are available for students to choose according to their schedule and preference.

To provide more choices for students according to the development of the global IT industry and the demands of the market, stream(s) are introduced in this programme. Based on their career goal and study plan, students enjoy the flexibility to study a MScIT degree with stream(s) or without stream if they fulfil the stream requirements.

• Stream in Artificial Intelligence and Big Data• Stream in Artificial Intelligence and Financial Technology

Excellent platform for peer learning and exchangeOur programme offers a comprehensive curriculum with cutting-edge subjects to cater for students with different career developmental needs. With such an environment of broad student mix, students benefit not only from learning the fundamental theories, core and applied technologies, and industry best practices but also from interaction with their peers in exchanging ideas and sharing experiences. COMP also maintains an extensive network of MSc alumni, students can acquire both advanced expertise and professional networks that help them scale new heights in their careers.

PROGRAMMESTRUCTUREStudents can select from a range of subjects offered under the programme. In general, each subject takes place once a week in the evening over a 13-week semester. Full-time students normally take 4 subjects in a semester and complete the study in 1.5 years, whereas part-time students usually take 2 subjects in a semester and complete the study in 2.5 years.

Mode of studyThis is a mixed mode programme that students are required to take 9 credits or more in a semester in order to retain full-time status or they will be given a part-time status. Non-local students subject to visa requirements are required to have full-time status (i.e. to take at least 9 credits for each semester).

Award requirementsTo fulfil the graduation requirement, students can choose to study a MScIT degree with stream(s)# or without stream by taking the required subjects.

Programme Core subjects: P-coreStream Core subjects: S-core

AwardsMScIT with Stream in

Artificial Intelligence and Big Data

MScIT with Stream in Artificial Intelligence and

Financial TechnologyMScIT

With Dissertation3 P-Core +4 S-Core +

1 Dissertation

3 P-Core +4 Electives* +1 Dissertation

With Project

3 P-Core +4 S-Core +

1 Elective* +1 Project

3 P-Core +5 Electives* +

1 Project

Without Dissertation/ Project

3 P-Core +4 S-Core +

3 Electives*

3 P-Core +7 Electives*

Credit Requirement 30 30

*Students can take master level elective subjects within the MScIT programme to satisfy their elective requirements, subject to the pre-requisite and exclusion requirements.

#The streams will not form a part of the official award parchment. A seperate certificate will be issued upon completion of stream.

For more information about the curriculum, please visit www.comp.polyu.edu.hk/en-us/prospective-students/taught-postgraduate-programmes.

List of subjectsWhile every stream has its own group of core subjects, a range of electives is offered depending on the availability of teaching resources and the number of registered students. Students are allowed to choose from a common pool of electives within the Department of Computing, subject to vacancies available.

All subjects below bear three credits unless otherwise stated and they are subject to review and changes.

Programme core subjects: P-coreStream core subjects: S-coreElective subjects: E

MSc/ PgD IT Subject

MScIT with Stream in Artificial

Intelligence and Big Data

MScIT with Stream in Artificial

Intelligence and Financial Technology

MScIT

1 Data Structures and Database Systems P-Core P-Core P-Core

2 Software Engineering and Development P-Core P-Core P-Core

3 Internet Infrastructure and Protocols P-Core P-Core P-Core

4 Artificial Intelligence Concepts S-Core S-Core E

5 Big Data Computing S-Core E E

6 Machine Learning and Data Analytics S-Core E E

7 Computer Vision and Image Processing S-Core E E

8 Financial Computing E S-Core E

9 Cyber and Internet Security E S-Core E

10 Distributed Ledger Technology, Cryptocurrency and E-Payment E S-Core E

11 Advanced Data Analytics E E E

12 Optimization and Applications E E E

13 Natural Language Processing E E E

14 Human Computer Interaction E E E

15 Wireless Networking and Mobile Computing E E E

16 Software Project Management E E E

17 Internet Computing and Applications E E E

18 Multimedia Computing, Systems and Applications E E E

19 Independent Study E E E

20 Project (6 credits) E E E

21 Dissertation (9 credits) E E E

22 Principles of Corporate Finance^ --- E ---

23 Investments^ --- E ---

24 Applications of Computing and Technology in Accounting and Finance I^ --- E ---

25 Business Analytics in Accounting and Finance^ --- E ---

^These subjects are offered by School of Accounting and Finance of PolyU.

Credit transferStudents may be given credits for recognised previous study at postgraduate level. The consideration for credit transfer is subject to the maximum validity period of 8 years and the grade performance at B or above. Students should submit an application of credit transfer within the first semester after admission, and it is subject to approval by the Programme Leader.

OUR STRONG TEAMFaculty Members Research Interests

Dr AU Man Ho AllenPhD(Wollongong); MIEEE Information Security and Privacy, Applied Cryptography, Cloud Security, Blockchain

Prof. BACIU GeorgePhD(Waterloo); MACM; MIEEE

Computer Graphics, Virtual Reality, Data Visualization, Collision Detection, Motion Synthesis and Dynamics of Large-Scale Deformable Surfaces, Virtual Clothing, Geometric Modeling, Texture Analysis and Image Processing

Prof. CAO JiannongPhD(Washington State); FIEEE; DMACM; SMCCF

Parallel and Distributed Computing, Wireless Networks and Mobile Computing, Big Data and Cloud Computing, Pervasive Computing, Fault Tolerant Computing

Dr CAO YixinPhD(TAMU); MACM; MSIAM Algorithmic Graph Theory, Combinatorial Optimization, Social Networks, Bioinformatics

Dr CHAN Chun Bun HenryPhD(British Columbia); MIEEE Networking and Communications, Cloud Computing, Internet Technologies, Electronic Commerce

Prof. CHAN Chun Chung KeithPhD(Waterloo)

Machine Learning, Artificial Intelligence, Big Data Analytics, Bioinformatics and Computational Biology, Evolutionary Computation, Fuzzy Systems, Software Engineering

Dr CHANG Kow Chuen RockyPhD(Rensselaer); MIEEE; MACM

Network Measurement Systems, Internet Infrastructure Security and Privacy, Network Operations and Management

Dr CHUNG Fu Lai KorrisPhD(CUHK); MIEEE

Data Mining, Machine Learning, Big Data Analytics, Computational Intelligence, Pattern Recognition, Multimedia

Dr GUAN NanPhD(Uppsala); MIEEE Real-Time Embedded Systems, Cyber-Physical Systems

Prof. GUO SongPhD(uOttawa); SMIEEE; SMAC Big Data, Wireless Communications and Mobile Computing, Cloud Computing, and Distributed System

Prof. HOORN JohanPhD(D. Litt.); PhD(D. Sc.) (Vrije University Amsterdam) Social robotics, affective computing, artificial creativity, and fundamentals of data analysis

Dr JANSSON JesperPhD(Lund) Graph Algorithms, Data Structures, Bioinformatics

Dr LEONG Hong-vaPhD(California); MACM; MIEEE Parallel and Distributed Computing, Distributed Databases, Mobile Computing, Internet Computing

Dr LI ShuaiPhD(Stevens)

Robotics, Dynamic Systems and Control, Recurrent Neural Networks, Distributed Control and Optimization

Dr LI Wenjie MaggiePhD(CUHK); MACL

Natural Language Processing, Social Media Mining, Text Mining, Information Retrieval, Extraction and Summarization

Dr LIU Yan FionaPhD(Columbia) Machine Learning, Multimedia Understanding

Dr LOU WeiPhD(Florida Atlantic); MIEEE Mobile Ad Hoc and Sensor Networks, Computer Networks, Mobile Computing, Multimedia Systems

Prof. LU QinPhD(UIUC); MH

Natural Language Process and Computational Linguistics, Lexical Semantics, Information Extraction and Knowledge Discovery, Open Systems and Standardization

Dr LUK Wing Pong RobertPhD(Southampton); FBCS; SMACM; SMIEEE, CEng, CITP Information Retrieval, Pattern Recognition, Natural Language Processing, Data Structures and Algorithms

Dr LUO Xiapu DanielPhD[PolyU(H.K.)]; MIEEE

Mobile Security and Privacy, Network Security and Privacy, Software Engineering, Blockchain/Smart Contracts, Internet Measurement, and Cloud Computing

Dr NGAI GracePhD(Johns Hopkins) Human Computer Interaction, Human Centered Computing, Natural Language Processing

Dr PATHAK Ajay KumarPhD(HKU); FIEEE; FIAPR Biometrics, Computer Vision-Based Industrial Inspection

Dr PEI Yu MaxPhD(Nanjing); PhD(ETH Zurich)

Automated Program Repair, Automated Software Testing, Mining Software Repositories, Object-oriented Techniques

Dr SHIU Chi Keung SimonPhD[PolyU(H.K.)]; MBCS; MACM; MHKCS; MIEEE

Artificial Intelligence, Coloured Petri Nets, Computer Games, Machine Learning, Pattern Recognition, Services Science and Management

Dr WANG DanPhD(Simon Fraser) Internet Architecture and Protocols, Computer Networking, Smart City, Big Data

Dr WANG QixinPhD(UIUC) Real-time/Embedded Systems, Cyber-Physical Systems

Dr WU XiaomingPhD(Columbia)

Machine learning, pattern recognition, and data mining, with recent focus on learning algorithms on graphs and their applications in computer vision and information retrieval

Dr XIAO BinPhD(UT Dallas)

Mobile Cloud Computing, Smartphone Technology, Data Management, Network Security, RFID Systems, Wireless Networks

Dr YANG LeiPhD(Xi’an Jiaotong) RFID System, Pervasive and Wireless Computing, Internet of Things, Smart Home

Dr YIU Man Lung KenPhD(HKU) Data Engineering, Query Processing, Spatial Database Systems

Prof. YOU Jia JanePhD(La Trobe); MIEEE Image Processing, Pattern Recognition, Computer-Aided Diagnosis and Monitoring

Prof. ZHANG LeiPhD(Northwestern Polytechnical); FIEEE Computer Vision, Pattern Recognition, Deep Learning, Image and Video Processing

Dr ZHENG YuanqingPhD(NTU); MIEEE, MACM Human Centered Computing, Mobile and Network Computing, Wireless Networks, RFID Systems

ADMISSIONREQUIREMENTS

• Applicants should possess a Bachelor’s degree in Computing / Computer Science, Mathematics, Information Systems, Engineering, or other Science disciplines. Applicants with a Bachelor’s degree in other disciplines with at least five years significant IT relevant work experience will also be considered.

• If you are not a native speaker of English, and your Bachelor’s degree or equivalent qualification is awarded by institutions where the medium of instruction is not English, you are expected to fulfil the following minimum English language requirement for admission purpose:

ᡩ A Test of English as a Foreign Language (TOEFL) score of 80 for the Internet-based test or 550 for the paper-based test; OR

ᡩ An overall Band Score of at least 6 in the International English Language Testing System (IELTS).

More information can be found at www.polyu.edu.hk/study.

Application proceduresTo apply for the programme, applicants can submit their application via an online admission system at www.polyu.edu.hk/admission starting from late September. This programme has a quota for admission therefore early application is strongly encouraged.

Programme code: 61030

Tuition fee and financial assistanceLocal students: HK$3,600 per creditNon-local students: HK$4,400 per credit

Some subjects are included in the reimbursable course list of the Continuing Education Fund (CEF). Eligible applicants will be reimbursed 80% of their fees, subject to a maximum sum of HK$10,000 (whichever is the less), on successful completion of a reimbursable course.

For detail information, please visit www.wfsfaa.gov.hk/cef/en/index.htm.

Envision Future COMPUTING

Computing for the FUTURE

Contents of this publica�on are subject to review and change.

PolyU COMP

M a s t e r o f S c i e n c e i n

INFORMATIONTECHNOLOGY資訊科技理學碩士學位課程

ARTIFICIAL INTELLIGENCEBIG DATA ANALYTICSFINANCIAL TECHNOLOGYCYBER SECURITY & CRYPTOCURRENCYCLOUD COMPUTINGNATURAL LANGUAGE PROCESSINGSOFTWARE DEVELOPMENT & MANAGEMENT

Department of ComputingPQ806

Mong Man Wai BuildingThe Hong Kong Polytechnic University

Hung Hom KowloonHong Kong

comp.enquiry@polyu.edu.hkwww.comp.polyu.edu.hk

Stream in AI and Big Data

Stream in AI and FinTech