for Research Postgraduate Students
The course requirements of the department are currently in a stage of transition to a new system. Therefore the requirements are different for students enrolled at different times.
Note: Core Research Courses (CRC) are full paper courses, covering topics fundamental to all fields of computer science. Research Oriented Courses (ROC) are half-paper courses, designed to introduce advanced topics in particular fields. These two types of courses are mainly aimed for M.Phil. and Ph.D. students.
Core Research Courses (CRC) in the Department of Computer Science
1. CSIS9101. Data Analysis and Machine Learning
Syllabuses of Core Research Courses (CRC)
CSIS9101. Data Analysis and Machine Learning
CSIS9301. Systems Design and Implementation
This course presents the principles behind the design and building of computer systems, in particular systems needed in the process of tackling a research problem. Some of these principles lead directly to objectives and properties that are desirable in systems of any type, including correctness, scalability, high performance, ease of use, trustworthiness and reliability. Some important implementation techniques that are applicable to a wide range of systems will be discussed. To assess a system against its objectives, rigorous testing and evaluation procedures need to be followed, which are part of the focus of the course.
CSIS9601. Theory of Computation and Algorithms Design
This course presents principles of theoretical computer science focusing on algorithmic
design and complexity analysis. Topics include: theoretical models of computation; computational complexity; design and analysis of algorithms and data structures (possible topics:
graphs, pattern matching, computational geometry); approximation and online algorithms.
CSIS9602. Convex Optimization
This course presents the theory, algorithms and applications of convex optimization. Main topics to be included: convex sets and functions; linear programming; quadratic programming, semidefinite programming, geometric programming; vector optimization; integer programming; duality and Lagrangian relaxation; Newton's method; interior point method; ellipsoid method; subgradient algorithm and decomposition method.