Postgraduate Courses

The goal of this course is to introduce how various analysis techniques can be used to manage the quality of a software application. Students will acquire fundamental knowledge of program abstraction, features, verification, testing, refactoring, concurrency, reliability, aspect orientation, and fault analysis. The course will also discuss how to carry out the empirical experimentation for program analysis. Wherever applicable, concepts will be complemented by tools developed in academia and industry. This enables students to understand the maturity and limitations of various analysis techniques.

Introduction to parallel computer architectures; principles of parallel algorithm design; shared-memory programming models; message passing programming models used for cluster computing; data-parallel programming models for GPUs; case studies of parallel algorithms, systems, and applications; hands-on experience with writing parallel programs for tasks of interest.

This advanced AI course will cover advanced concepts and techniques in AI. The major topics will be: problem solving, knowledge and reasoning, planning, uncertain knowledge and reasoning, learning, and robotics.

Introduction to major learning paradigms and techniques, basic applied statistics and information theory, decision trees, neural networks, Bayesian classification, kernel methods, clustering, density estimation, feature selection and extraction, hidden Markov models, reinforcement learning, case-based learning, model selection and various applications.

Bayesian networks and probabilistic modeling of complex domains; conditional independence and graph separation; variable elimination, clique tree propagation, and other inference algorithms; parameter learning; structure learning; latent structure models; recent developments.

Techniques for parsing, interpretation, context modeling, plan recognition, generation. Emphasis on statistical approaches, neuropsychological and linguistic constraints, large text corpora. Applications include machine translation, dialogue systems, cognitive modeling, and knowledge acquisition.

Introduction to the relational model and SQL. System architectures and implementation techniques of database management systems: disk and memory management, access methods, implementation of relational operators, query processing and optimization, transaction management and recovery. Hands on experience with building the components of a small DBMS.

Big data foundation; big data infrastructure; cloud computing, parallel computing, and stream computing for big data; big data extraction and integration; big data management; big data mining; visual analytics of big data; big data platforms and tools; big data applications in business intelligence, smart city, and bioinformatics; hands-on experience with big data from real world.

An introduction to knowledge discovery in databases. Different discovery and learning techniques are presented and compared. Automatic generation of query language expressions is discussed in depth. Potential applications are shown.

The first part of this course covers an introduction to mathematical tools and computational techniques for image synthesis and manipulation of 3D models. The second part covers more advanced topics which may include digital geometry processing, image processing, visualization, GPU computing, numerical optimization methods.

Introduction to techniques for automatically describing visual data and tools for image analysis; perception of spatial organization; models of general purpose vision systems; computational and psychological models of perception.

Principles, design and implementation of computer communication networks; network architecture and protocols, OSI reference model and TCP/IP networking architecture; Internet applications and requirements; transport protocols, TCP and UDP; network layer protocols, IP, routing, multicasting and broadcasting; local area networks; data link and physical layer issues; TCP congestion control, quality of service, emerging trends in networking.

Advanced principles in computer and communication networking: Internet multicast, overlay and peer-to-peer networks; wireless and mobile computing, multimedia networking, network security, selected topics of current interests: wireless protocols, wireless security, sensor networks, cloud computing, data centers, software-defined networks, network implementation, etc.

Classical encryption techniques, block and stream ciphers, public-key cryptography, authentication, nonrepudiation, key management, digital signatures, public key infrastructure, cryptographic protocol, secret sharing, electronic mail security, IP security, Web security, Firewalls, Intrusion detection.

This is an introductory graduate course in algorithmic techniques. Topics include: advanced data structures; graph algorithms; amortization; approximation algorithms; on-line algorithms; randomized and probabilistic analysis.

An introduction to the basic tools of combinatorial optimization, including network flow and the max-flow min-cut theorem, linear programming, matching, spanning trees and matroids, dynamic programming, algorithms and data structures, graph algorithms.

An introductory course in Computational Geometry. Algorithms for manipulating geometric objects. Topics include Convex Hulls, Voronoi Diagrams, Point Location, Triangulations, Randomized Algorithms, Point-Line Duality.

Selected topics in software engineering of current interest to the Department and not covered by existing courses.

Advanced topics in artificial intelligence including neural networks, natural language processing, logic programming, image understanding, robotics and others.

Selected topics in database systems of current interest to the Department and not covered by existing courses.

Selected topics in graphics of current interest to the Department and not covered by existing courses.

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.

Advanced topics in communication networks, including issues in high speed networking, ATM, multimedia communication, network interconnection, network management, and protocol verification and testing.

Selected topics in computer engineering of current interest to the Department and not covered by existing courses.

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.

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.

This one-credit course aims at providing research postgraduate students with basic training in teaching skills, research management, career development, and related professional skills. This course consists of a number of mini-workshops. Some department-specific workshops will be coordinated by Department of CSE. Graded PP, P or F.

A regular seminar presenting research problems currently under investigation. Students are expected to attend regularly. Graded P or F.

Continuation of COMP 6911. Graded P or F.

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.

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.)

Master's thesis research supervised by a faculty member. A successful defense of the thesis leads to Pass. No course credit is assigned.

Original and independent doctoral thesis research supervised by a faculty member. A successful defense of the thesis leads to Pass. No course credit is assigned.