Postgraduate Courses

On successful completion of the course, students will be able to:

• 1.
  Understand the principles of software analysis and testing.
• 2.
  Demonstrate capability to use automated tools on real-life software applications effectively for software analysis and testing.
• 3.
  Develop an automated software tool applying the principles of software analysis and testing.
• 4.
  Demonstrate capability to interpret and communicate the research outcome of the latest development on software analysis and testing.

On successful completion of the course, students will be able to:

• 1.
  Demonstrate knowledge of parallel computer architectures.
• 2.
  Develop understanding of principles of parallel algorithm design.
• 3.
  Demonstrate knowledge of shared-memory and distributed-memory programming models.
• 4.
  Write parallel programs of a computation task using learned programming languages.

On successful completion of the course, students will be able to:

• 1.
  Demonstrate a systematic overall understanding of the field of artificial intelligence.
• 2.
  Demonstrate systematic knowledge of artificial intelligence.
• 3.
  Demonstrate broad knowledge in all major areas of AI including agent design, search, machine learning and knowledge representation and reasoning.
• 4.
  Demonstrate an ability to combine various AI techniques in problem solving.

On successful completion of the course, students will be able to:

• 1.
  Demonstrate a systematic overall understanding of the machine learning field.
• 2.
  Demonstrate an in-depth understanding of the fundamental issues and principles in machine learning.
• 3.
  Demonstrate an in-depth understanding of core and recent machine learning algorithms.
• 4.
  Demonstrate an ability to comprehend and develop novel machine learning models and methods.
• 5.
  Demonstrate an ability to apply machine learning algorithms to solve real-world problems.

On successful completion of the course, students will be able to:

• 1.
  Demonstrate a systematic understanding of Bayesian networks and, more generally, probabilistic unsupervised learning.
• 2.
  Demonstrate an ability to comprehend and develop novel models and methods for probabilistic unsupervised learning.
• 3.
  Demonstrate an ability to apply probabilistic unsupervised learning models and methods to solve real-world problems.

On successful completion of the course, students will be able to:

• 1.
  Demonstrate a solid understanding of scientific theory and methods relevant to ECE.
• 2.
  Demonstrate sound knowledge of relevant scientific literature.
• 3.
  Apply theories, methodologies, and knowledge to address important research questions relevant to ECE.
• 4.
  Demonstrate practical skills in designing or developing ECE systems.
• 5.
  Demonstrate the ability to independently pursue original research and innovation.
• 6.
  Demonstrate oral and written communication skills sufficient for a professional career in ECE disciplines.

On successful completion of the course, students will be able to:

• 1.
  Possess holistic understanding of the fundamental concepts of natural language processing and machine translation, and grasp how it stress tests all aspects of human intelligence and language processing.
• 2.
  Firmly grasp multiple input-output formulations of transduction, such as alignment, chunking, classification, dependency relations, and parsing, and the relationship between noisy channel and loglinear models of string transduction, and their Bayesian interpretations.
• 3.
  Possess systematic understanding of the relationship between word segmentation and phrasal lexicons, the relationship to transduction and alignment, and associated algorithms; the relationship between traditional grammatical formalisms versus stochastic and weighted grammars; the strengths and weaknesses of part-of-speech models, and associated tagging algorithms; and the various fundamental approaches to parsing, and how they deal with syntactic ambiguity.
• 4.
  Possess advanced understanding of how bilingual models of syntax generalize upon monolingual models to improve learnability; the combinatorial and empirical trade-offs between various learning models of alignment and compositionality, and their associated algorithms; and the core methods for inducing lexicons, translation lexicons, phrasal translation lexicons, as well as permutation and reordering models.
• 5.
  Possess advanced understanding of the combinatorial and empirical trade-offs between various runtime models for translation and their associated algorithms, and how bilingual transduction models generalize upon monolingual parsing models.
• 6.
  Possess holistic understanding of lexical semantics models for word sense disambiguation, their relationship to phrasal lexicons and transduction, and associated ambiguity resolution algorithms, as well as for semantic frames (predicate-argument structures), and associated semantic role labeling algorithms.

On successful completion of the course, students will be able to:

• 1.
  Demonstrate machine learning algorithm design skills for data analytics tasks.
• 2.
  Analyze the quality of results to domain problems.
• 3.
  Develop a program that can handle existing real problems.

On successful completion of the course, students will be able to:

• 1.
  Discuss the importance and complexity of probabilistic modelling and information processing.
• 2.
  Develop first-hand know-how during class about modern robotic platforms, such as Turtlebot.
• 3.
  Demonstrate good comprehension of fundamentals of basic machine learning techniques by lecture assignments.
• 4.
  Demonstrate the ability to apply practical algorithms to realise basic perception for robotic systems by projects.
• 5.
  Apply knowledge in computer science, signal processing and practical programming.
• 6.
  Use the aggregate knowledge to perform robotic tasks to operate simulated robots in simulated environment scenarios.

On successful completion of the course, students will be able to:

• 1.
  Demonstrate a deep understanding of the field of data management. Learn concepts related to data base systems, applications and algorithms.
• 2.
  Learn current research trends in data management research and obtain the ability to produce novel research and engineering results.
• 3.
  Through presentations and literature surveys, the students will improve their communication skills. They are also expected to clearly interpret research or engineering concepts to specialist and general audiences.
• 4.
  The students will learn research topics and techniques related to data management, which will enhance their ability as researchers in academia, or practitioners in industry.
• 5.
  Learn the principles of ethics, including appropriate citations of related work and repeatability of experiments.
• 6.
  Acquire sufficient knowledge of data management for undergraduate teaching and assessment of student learning.

On successful completion of the course, students will be able to:

• 1.
  Describe basic concepts about data mining and knowledge discovery.
• 2.
  Apply clustering algorithms to cluster relational data.
• 3.
  Apply classification algorithms to relational data.
• 4.
  Use frequent pattern mining solutions to identify frequent patterns and association rules.
• 5.
  Apply outlier detection methods to detect outliers.

On successful completion of the course, students will be able to:

• 1.
  Understanding of algorithms and mathematics for representing, manipulating and analyzing geometric data.
• 2.
  Understanding how the GPU's rendering pipeline operates.
• 3.
  Understanding of algorithms and mathematics for advanced real-time rendering.
• 4.
  Ability to explain principles and different techniques used for real-time rendering and understand their trade-offs.
• 5.
  Ability to design and implement graphics applications for geometry processing and real-time rendering.

On successful completion of the course, students will be able to:

• 1.
  Implement practical various AI solutions by applying relevant computer vision algorithms.
• 2.
  Demonstrate capability of visual recognition to robotics and intelligent systems.
• 3.
  Identify key problems in contemporary computer visionfor further research.
• 4.
  Develop practical computer vision systems for various recognition tasks such as autonomous driving and video surveillance.

On successful completion of the course, students will be able to:

• 1.
  Understand and explain data communications networks, their components, the technologies behind them, the network organization.
• 2.
  Identify and classify the different functionalities necessary for building viable communication networks, and be able.
• 3.
  Explain the TCP/IP protocol family and how each of the TCP, UDP and IP protocols contribute to implementing the Internet.
• 4.
  Understand the difference between different switching techniques (packet switching vs. Circuit switching).
• 5.
  Understand the factors that impact network performance and how to strike a balance between complexity and performance.
• 6.
  Understand the basic principles behind network applications.
• 7.
  Understand the principles behind transport layer protocols, the services they provide, and build an understanding of basic.
• 8.
  Explain the differences between routing and forwarding and how a router functions.
• 9.
  Show an understanding of intra-domain and inter-domain routing and how these are instantiated in the Internet through protocols.
• 10.
  Show an understanding of the principles behind the link layer, its instantiation and implementation in various networking technologies.

On successful completion of the course, students will be able to:

• 1.
  Demonstrate a systematic understanding about the advanced topics in networking area.
• 2.
  Demonstrate knowledge and understandning of selected topics in networking areas, including p2p, overlay, multimedia networking, wireless networks, security, and IoT networking.
• 3.
  Demonstrate knowledge and understanding for how to conduct a networking related research.

On successful completion of the course, students will be able to:

• 1.
  Demonstrate a systematic understanding of cryptography and security.
• 2.
  Get familiar with real-world security systems, and be able to handle real-world security events.
• 3.
  Establish the theory foundation for doing research in security-related topics.
• 4.
  Improve communication capability in writing and oral presentations.

On successful completion of the course, students will be able to:

• 1.
  Demonstrate an understanding of the theory of advanced algorithm design and analysis techniques.
• 2.
  Demonstrate an ability to design algorithms using techniques such as randomization, hashing, and online algorithms.
• 3.
  Demonstrate an ability to analyze algorithms using parameterization, amortization, and approximation.

On successful completion of the course, students will be able to:

• 1.
  Demonstrate an understanding of the mathematical theory underlying algorithms for combinatorial optimization.
• 2.
  Demonstrate an ability to use linear and integer programming to formulate different combinatorial problems.
• 3.
  Demonstrate an ability to apply various techniques to design approximation algorithms for hard problems.

On successful completion of the course, students will be able to:

• 1.
  Demonstrate ability to solve basic problems in computational geometry.
• 2.
  Understand some fundamental algorithmic techniques in computational geometry.
• 3.
  Demonstrate ability to design and analyse algorithms using the fundamental techniques in computational geometry.

On successful completion of the course, students will be able to:

• 1.
  Demonstrate knowledge and understanding of selected topics in software engineering of current interest to the Department and not covered by existing courses.

On successful completion of the course, students will be able to:

• 1.
  Demonstrate knowledge and understanding of advanced topics in artificial intelligence including neural networks, natural language processing, logic programming, image understanding, robotics and others.

On successful completion of the course, students will be able to:

• 1.
  Demonstrate knowledge and understanding of selected topics in database systems of current interest to the Department and not covered by existing courses.

On successful completion of the course, students will be able to:

• 1.
  Demonstrate knowledge and understanding of selected topics in graphics of current interest to the Department and not covered by existing courses.

On successful completion of the course, students will be able to:

• 1.
  Demonstrate knowledge and understanding of advanced topics in computer systems analysis; issues in the development and solution of system models; model parametrization, verification and validation; recent developments in techniques and tools for system evaluation.

On successful completion of the course, students will be able to:

• 1.
  Demonstrate knowledge and understanding of advanced topics in communication networks, including issues in high speed networking, ATM, multimedia communication, network interconnection, network management, and protocol verification and testing.

On successful completion of the course, students will be able to:

• 1.
  Demonstrate knowledge and understanding of selected topics in computer engineering of current interest to the Department and not covered by existing courses.

On successful completion of the course, students will be able to:

• 1.
  Demonstrate knowledge and understanding of selected topics in applications of computer science not covered by existing course. Credits earned by taking this course can only be used to satisfy the breath requirement of the research area of Software and Applications.

On successful completion of the course, students will be able to:

• 1.
  Demonstrate knowledge and understanding of selected topics in theoretical computer science not covered by existing courses, including, but not limited to, computational complexities and computability, graph algorithms and combinatorial optimization.

On successful completion of the course, students will be able to:

• 1.
  Demonstrate a systematic understanding of the field of computer science, and wide knowledge across the three major areas including systems, applications and theory.
• 2.
  Communicate effectively and professionally both in writing and oral presentations using appropriate technical languages, and interpret their research outcome both to a specialist and a general audience clearly.
• 3.
  Follow the principles of ethics in their field and in academia.

On successful completion of the course, students will be able to:

• 1.
  Demonstrate a systematic understanding of the field of computer science, and wide knowledge across the three major areas including systems, applications and theory.
• 2.
  Communicate effectively and professionally both in writing and oral presentations using appropriate technical languages, and interpret their research outcome both to a specialist and a general audience clearly.
• 3.
  Follow the principles of ethics in their field and in academia.

On successful completion of the course, students will be able to:

• 1.
  Demonstrate knowledge and understanding of an independent research project carried out under the supervision of a faculty member. This course is only available for exchange, visiting and visiting internship students.

On successful completion of the course, students will be able to:

• 1.
  Demonstrate knowledge and understanding of an independent research project carried out under the supervision of a faculty member. (Only one independent studies course may be used to satisfy the course requirements for any postgraduate program.)

On successful completion of the course, students will be able to:

• 1.
  Demonstrate a general understanding of the field of computer science, and knowledge in one of the three major areas including systems, applications and theory.
• 2.
  Make a contribution to their specific field of study through either original research or substantial engineering efforts.
• 3.
  Communicate effectively and professionally both in writing and oral presentations using appropriate technical languages, and interpret their research or engineering outcome both to a specialist and a general audience clearly.
• 4.
  Follow the principles of ethics in their field and in academia.
• 5.
  Demonstrate a mastery of skills and knowledge at a level required for college and university undergraduate teaching in computer science and assessment of student learning.

On successful completion of the course, students will be able to:

• 1.
  Demonstrate original contributions to their specific fields of research in their doctoral thesis.