Course List

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Evening Course List

Course Code	Course Title
CMSC5702	Advanced Topics in Parallel / Distributed Systems
CMSC5707	Advanced Topics in Artificial Intelligence
CMSC5711	Image Processing and Computer Vision
CMSC5713	IT Project Management
CMSC5716	Web Based Graphics & Virtual Reality Systems
CMSC5718	Introduction to Computational Finance
CMSC5719	Seminar
CMSC5720	Project I
CMSC5721	Project II
CMSC5724	Data Mining and Knowledge Discovery
CMSC5726	Computer and Network Security
CMSC5727	*Computer Game Software Production
CMSC5728	Decision Analysis & Game Theory
CMSC5730	IT Entrepreneurship and Marketing
CMSC5733	Social Computing
CMSC5734	Network Science
CMSC5735	Advanced Topics in Cloud Computing
CMSC5741	Big Data Technology and Applications
CMSC5743	Efficient Computing of Deep Neural Networks
CMSC5744	Internship

*This course has been included in the list of reimbursable courses under the Continuing Education fund. This course is recognized under the Qualifications Framework (QF Level 6).

CMSC5702 Advanced Topics in Parallel / Distributed Systems

This course covers selected topics in parallel/distributed systems. The detailed contents may be changed from year to year depending on the current development and the teacher specialty.

CMSC5707 Advanced Topics in Artificial Intelligence

This course will cover selected topics from: advanced pattern recognition, neural networks, expert systems and fuzzy systems, evolutionary computing, learning theory, constraint processing, logic programming, probabilistic reasoning, computer vision, speech processing, and natural language processing. (Not for students who have taken CSCI6200 or ENGG5189.)

CMSC5711 Image Processing and Computer Vision

This first part of the course includes fundamental topics in image processing, e.g., image enhancement techniques, color image processing, image segmentation, and image compression. The second part of the course focuses on topics concerning methodologies of recovering 3D information from 2D images. Specifically, techniques for camera calibration, stereoposis, motion analysis, pose estimation and structure from motion will be discussed. These techniques will have practical applications to virtual reality, model reconstruction and graphics. (Not for students who have taken CSCI5280 or ENGG5104.)

CMSC5713 IT Project Management

This course covers the key elements of the project management framework related to information technology. Topics include the identification of elements and processes of project management, processes involved in project integration management, project scope management, various tools and techniques used to develop project schedules and resource planning, processes of project quality management, project communications management, risk management, human resources management, and project procurement management. Experts from industry will also be invited to share their experience in the topics. (Not for students who have taken DSME6730)

CMSC5716 Web Based Graphics & Virtual Reality Systems

This course aims to provide students the platform to learn about interactive media and virtual reality with emphasis on their internet applications. Up-to-date interactive media techniques, including modeling, rendering, illumination, texture mapping, animation, and visualization, will be introduced in the first-half of the course. The second-half course will focus on the Web-based VR interfaces which build up the fundamental basis for testing new ideas and alternative solution for the latest VR research, including VR scene modeling, dynamic objects, interactive navigation and sensors, real-time rendering, and diversifed web-based VR applications. (Not for students who have taken CSCI5460.)

CMSC5718 Introduction to Computational Finance

This course introduces some basic concepts in computational finance. Topics include risk and return, modern portfolio theory, calculating the efficient frontier, multiple factor models, various models for portfolio optimization, utility functions and evaluation of portfolio performance.

CMSC5719 Seminar

The seminar is a series of 12 sessions with speakers invited from academia or industry to present a range of current topics in computer science to widen the students’ horizon and perspectives.

CMSC5720 Project I

The project provides a challenge for students to apply their computing knowledge and expertise to carry out independent research and development work in any area of Computer Science. A project report has to be written under the supervision of the lecturing staff. (Students must take both CMSC5720 and CMSC5721 in order to have the credits counted towards graduation.)

CMSC5721 Project II

CMSC5724 Data Mining and Knowledge Discovery

This course introduces the techniques used in data mining. Topics include clustering, classification, estimation, forecasting, statistical analysis and visualization. Data Mining provides useful tools for the analysis and visualization. Data Mining provides useful tools for the analysis, understanding and extraction of useful information from huge databases. Applications range from business, finance, medicine and engineering. (Not for students who have taken CSCI5180 or ENGG5103.)

CMSC5726 Computer and Network Security

Issues of computer and network security. Weaknesses of network protocols. Security protocols. Firewalls. Computer viruses. System security threats. Applications of Cryptography. (Not for students who have taken CSCI5470 or ENGG5105 or CENG5240.)

CMSC5727 Computer Game Software Production

This course focuses on the programming issues in computer gaming software production, discusses the process in developing a game application and analyzes various considerations in technologies used. The main emphasis is on the real time performance requirement in computer game development. Indoor/outdoor rendering, networking, artificial intelligence, physics as well as the game design issues would be introduced. The students would further gain the production experience through the game development project in the course.

CMSC5728 Decision Analysis & Game Theory

This course introduces decision theory and game theory used in computer science, in particular, artificial intelligence and multiagent systems. Topics include utility theory, decision under risk, decisions under uncertainty, social choices, strategic games and Nash equilibrium, extensive games and subgame perfect equilibrium, repeated games and folk theorems, and applications in computer science. Prerequisite: CSCI2110 (or equivalent) and ENGG2040 (or equivalent). (Not for students who have taken CSCI5350.)

CMSC5730 IT Entrepreneurship and Marketing

This course equips students with the skills on how to create, launch and run an IT business and to develop a coherent marketing strategy. The key topics include entrepreneur characteristics, identifying market opportunity, IT product/service management, business planning, formation of a new IT venture, financing, market segmentation, positioning, pricing, promotion, distribution and channel management.

CMSC5733 Social Computing

This course introduces recent developments in the emerging field of social computing, which investigates the information processing of social signals that arise from the interactions among entities through social media technologies such as social networking sites, blogs, SMS, mails, virtual communities, multimedia sharing sites, mobile devices, etc. Topics include, but not limited to, social network theories, link analysis, learning to rank, graph algorithms, question and answering, recommender systems, etc. The students should have some knowledge in the area of machine learning, data mining, or other related fields.

CMSC5734 Network Science

This course introduces network science as an emerging discipline that studies the networks for revelation of organised knowledge in them, so that network behaviour and various phenomena can be predicted. Topics include the introduction of various networks, such as regular networks, random networks, small-world networks and scale-free networks. The emergence of networks will be discussed. Then various important issues in networks such as epidemics, synchrony, influence network, vulnerability, NetGain and biology are discussed.

CMSC5735 Advanced Topics in Cloud Computing

This course covers advanced topics in cloud computing. Topics will include new problems proposed in each year. Some topics to be discussed include: cloud computing models (e.g., SaaS, PaaS, IaaS); distributed and parallel data processing (e.g. MapReduce, Hadoop); data storage (cloud storage architectures, data centers, data deduplication); case studies of real-world cloud services (e.g. Amazon EC2, Windows Azure).

CMSC5741 Big Data Technology and Applications

This course aims at teaching students the state-of-the-art big data technology, including techniques, software, applications, and perspectives with massive data. The class will cover, but not be limited to, the following topics: advanced techniques in distributed file systems such as Google File System, Hadoop Distributed File System, and map-reduce technology; similarity search techniques for big data such as minhash, locality-sensitive hashing; specialized processing and algorithms for data streams; big data search and query technology; recommendation systems for Web applications. The applications may involve business applications such as online marketing, computational advertising, location-based services, social networks, recommender systems, healthcare services.

CMSC5743 Efficient Computing of Deep Neural Networks

The high computational demands of deep neural networks (DNNs) coupled with their pervasiveness across both cloud and IoT platforms has led to a rise in specialized hardware and software techniques to accelerate DNN executions. This course will present the techniques that enabling efficient applications and computing of DNNs. The course will start with an overview of DNNs, and then introduce various frameworks and architectures that support DNNs, as well as the implementations and optimizations on computing platforms.

CMSC5744 Internship

The objective of the course is to allow students to acquire a basic understanding and the skills of the practical aspects of computer science. To qualify for the award of the subject credits, the student must attach to a company in a computer science related post as approved by the Professor-in-Charge for no less than 12 weeks. The student will have an academic supervisor as assigned by the Professor-in-Charge and an industry supervisor from the company. There will be a mid-term company visit by the academic supervisor.

At the end of the internship, the student must give a presentation to the academic and industry supervisors, and submit a report summarizing what the student has done and learnt from the internship. The student’s grade will be determined by (1) the presentation, (2) the student report and (3) a testimonial from the industry supervisor.

The internship should normally take place in the summer term after a student has finished the first two semesters of studies. Part-time students can decide to undertake the internship in the summer term of either the first or second year of studies.

Students are recommended to seek the Professor-in-Charge’s comment on potential internship opportunities before enrolling in the course.