Please note:
To view the current Academic Calendar go to www.sfu.ca/students/calendar.html
Computing Science
The Master of Science (MSc) in Computing Science is a research-intensive program that has a primary emphasis on the MSc thesis. The program provides an environment for education in theoretical and applied Computer Science. Through training in formal coursework and hands-on research in areas such as artificial intelligence, computer systems and networks, computer graphics, and data mining, graduates will be capable of working with integrity to design, improve, and apply cutting-edge computational techniques to support a career in academia, industry or the public sector.
Admission Requirements
Applicants must satisfy the University admission requirements as stated in Graduate General Regulations 1.3 in the SFU Calendar and have a bachelor's degree or the equivalent in computing science or a related field.
Program Requirements
All students are admitted to the thesis option and then transferred to the project or course option with consent. Any change to a student's program must be approved by the school's graduate program committee.
This program consists of course work, an optional internship, and a thesis, project, or course option for a minimum of 30 units.
Students must complete one of
The objective of this course is to expose students to basic techniques in algorithm design and analysis. Topics will include greedy algorithms, dynamic programming, advanced data structures, network flows, randomized algorithms. Students with credit for CMPT 706 may not take this course for further credit.
This course provides a broad view of theoretical computing science with an emphasis on complexity theory. Topics will include a review of formal models of computation, language classes, and basic complexity theory; design and analysis of efficient algorithms; survey of structural complexity including complexity hierarchies, NP-completeness, and oracles; approximation techniques for discrete problems. Equivalent Courses: CMPT810.
and two courses from two other areas
and one course from any area
and an additional three units of graduate courses or internship from Table 2 with approval of supervisor and Graduate Chair
and the requirements from one of the three options below.
Thesis Option
and a thesis
Project Option
and two additional course from any area in Table 1
and an additional three units of graduate courses
and a project
Course Option
and an additional 12 units of graduate courses
and portfolio
Breadth Requirements
For purposes of defining breadth requirements, courses are grouped into the five major areas shown in Table 1. Courses not related to the breadth requirements are shown in Table 2. Any courses completed outside the School of Computing Science must be approved by the student's supervisor and the director of the graduate program.
Table 1
Area I – Algorithms and Complexity Theory
Deep connections between logic and computation have been evident since early work in both areas. More recently, logic-based methods have led to important progress in diverse areas of computing science. This course will provide a foundation in logic and computability suitable for students who wish to understand the application of logic in various areas of CS, or as preparation for more advanced study in logic or theoretical CS.
The objective of this course is to expose students to basic techniques in algorithm design and analysis. Topics will include greedy algorithms, dynamic programming, advanced data structures, network flows, randomized algorithms. Students with credit for CMPT 706 may not take this course for further credit.
This course provides a broad view of theoretical computing science with an emphasis on complexity theory. Topics will include a review of formal models of computation, language classes, and basic complexity theory; design and analysis of efficient algorithms; survey of structural complexity including complexity hierarchies, NP-completeness, and oracles; approximation techniques for discrete problems. Equivalent Courses: CMPT810.
Fundamental algorithmic techniques used to solve computational problems encountered in molecular biology. This area is usually referred to as Bioinformatics or Computational Biology. Students who have taken CMPT 881 (Bioinformatics) in 2007 or earlier may not take CMPT 711 for further credit.
This course covers recent developments in discrete, combinatorial, and algorithmic geometry. Emphasis is placed on both developing general geometric techniques and solving specific problems. Open problems and applications will be discussed.
Algorithm design often stresses universal approaches for general problem instances. If the instances possess a special structure, more efficient algorithms are possible. This course will examine graphs and networks with special structure, such as chordal, interval, and permutation graphs, which allows the development of efficient algorithms for hard computational problems.
This course will cover a variety of optimization models, that naturally arise in the area of management science and operations research, which can be formulated as mathematical programming problems. Equivalent Courses: CMPT860.
Area II – Networks, Software and Systems
This course examines fundamental principles of software engineering and state-of-the-art techniques for improving the quality of software designs. With an emphasis on methodological aspects and mathematical foundations, the specification, design and test of concurrent and reactive systems is addressed in depth. Students learn how to use formal techniques as a practical tool for the analysis and validation of key system properties in early design stages. Applications focus on high level design of distributed and embedded systems.
Investigates the design and operation of wide-area computer networks, especially the Internet and the TCP/IP protocol suite. This course studies performance modeling, security and quality of service; wireless connectivity and multimedia networking; network services, including recent topics and trends in these areas.
The goal of formal verification is to prove correctness or to find mistakes in software and other systems. This course introduces, at an accessible level, a formal framework for symbolic model checking, one of the most important verification methods. The techniques are illustrated with examples of verification of reactive systems and communication protocols. Students learn to work with a model checking tool such as NuSMV.
This course investigates the design, classification, modelling, analysis, and efficient use of communication networks such as telephone networks, interconnection networks in parallel processing systems, and special-purpose networks. Equivalent Courses: CMPT881.
Area III – Artificial Intelligence
Knowledge representation is the area of Artificial Intelligence concerned with how knowledge can be represented symbolically and manipulated by reasoning programs. This course addresses problems dealing with the design of languages for representing knowledge, the formal interpretation of these languages and the design of computational mechanisms for making inferences. Since much of Artificial Intelligence requires the specification of a large body of domain-specific knowledge, this area lies at the core of AI. Prerequisite: CMPT 310/710 recommended. Cross-listed course with CMPT 411.
Machine Learning is the study of computer algorithms that improve automatically through experience. Provides students who conduct research in machine learning, or use it in their research, with a grounding in both the theoretical justification for, and practical application of, machine learning algorithms. Covers techniques in supervised and unsupervised learning, the graphical model formalism, and algorithms for combining models. Students who have taken CMPT 882 (Machine Learning) in 2007 or earlier may not take CMPT 726 for further credit.
This course surveys current research in formal aspects of knowledge representation. Topics covered in the course will centre on various features and characteristics of encodings of knowledge, including incomplete knowledge, non monotonic reasoning, inexact and imprecise reasoning, meta-reasoning, etc. Suggested preparation: a course in formal logic and a previous course in artificial intelligence.
In this course, theoretical and applied issues related to the development of natural language processing systems and specific applications are examined. Investigations into parsing issues, different computational linguistic formalisms, natural language syntax, semantics, and discourse related phenomena will be considered and an actual natural language processor will be developed.
Intelligent systems are knowledge-based computer programs which emulate the reasoning abilities of human experts. This introductory course will analyze the underlying artificial intelligence methodology and survey advances in rule-based systems, constraint solving, incremental reasoning, intelligent backtracking and heuristic local search methods. We will look specifically at research applications in intelligent scheduling, configuration and planning. The course is intended for graduate students with a reasonable background in symbolic programming.
Area IV – Databases, Data Mining and Computational Biology
Introduction to advanced database system concepts, including query processing, transaction processing, distributed and heterogeneous databases, object-oriented and object-relational databases, data mining and data warehousing, spatial and multimedia systems and Internet information systems.
The student will learn basic concepts and techniques of data mining. Unlike data management required in traditional database applications, data analysis aims to extract useful patterns, trends and knowledge from raw data for decision support. Such information are implicit in the data and must be mined to be useful.
Examination of recent literature and problems in bioinformatics. Within the CIHR graduate bioinformatics training program, this course will be offered alternatively as the problem-based learning course and the advanced graduate seminar in bioinformatics (both concurrent with MBB 829). Prerequisite: Permission of the instructor.
An advanced course on database systems which focuses on data mining and data warehousing, including their principles, designs, implementations, and applications. It may cover some additional topics on advanced database system concepts, including deductive and object-oriented database systems, spatial and multimedia databases, and database-oriented Web technology.
Area V – Graphics, HCI, Vision and Visualization
Advanced topics in geometric modelling and processing for computer graphics, such as Bezier and B-spline techniques, subdivision curves and surfaces, solid modelling, implicit representation, surface reconstruction, multi-resolution modelling, digital geometry processing (e.g., mesh smoothing, compression, and parameterization), point-based representation, and procedural modelling. Prerequisite: CMPT 361, MACM 316. Students with credit for CMPT 464 or equivalent may not take this course for further credit.
Advanced topics in the field of scientific and information visualization are presented. Topics may include: an introduction to visualization (importance, basic approaches and existing tools), abstract visualization concepts, human perception, visualization methodology, 2D and 3D display and interaction and their use in medical, scientific, and business applications. Prerequisite: CMPT 316, 461 or equivalent (by permission of instructor). Students with credit for CMPT 878 or 775 may not take this course for further credit.
This seminar course covers current research in the field of multimedia computing. Topics include multimedia data representation, compression, retrieval, network communications and multimedia systems. Computing science graduate student or permission of instructor. Equivalent Courses: CMPT880.
A seminar based on the artificial intelligence approach to vision. Computational vision has the goal of discovering the algorithms and heuristics which allow a two dimensional array of light intensities to be interpreted as a three dimensional scene. By reading and discussing research papers - starting with the original work on the analysis of line drawings, and ending with the most recent work in the field - participants begin to develop a general overview of computational vision, and an understanding of the current research problems.
Explores current research in the field of imaging, computer vision, and smart cameras that aims at identifying, eliminating, and re-lighting the effects of illumination in natural scenes. One salient direction in this research is the identification and elimination of shadows in imagery. The topics touched on in the endeavour include physics-based image understanding, image processing, and information theory. Students in vision and in graphics should be interested in the material in this course.
Examines current research topics in computer graphics, human computer interaction (including audio), computer vision and visualization.
Table 2
An internship in industry or a research environment for graduate research students. A final report will be submitted and graded by the student's supervisor. Units of this course do not count towards computing science breadth requirements. Graded on a satisfactory/unsatisfactory basis. Prerequisite: 12 units of CMPT course work with an SFU CGPA of at least 3.0. Approval of supervisor and a GPC representative is required prior to applying for, or accepting an internship.
| Section | Instructor | Day/Time | Location |
|---|---|---|---|
|
May 11 鈥� Aug 10, 2020: Mon, Tue, Wed, Thu, Fri, 8:00 a.m.鈥�8:00 p.m.
|
Burnaby |
This course aims to give students experience to emerging important areas of computing science. Prerequisite: Instructor discretion.
NOTE: SFU students enrolled in the Accelerated Master's within the School of Computing Science may apply a maximum of nine graduate course units, taken while completing the bachelor's degree, towards the upper division undergraduate electives of the bachelor's program and the requirements of the master's degree. These courses need to be selected in consultation with the supervisor. For more information go to: /dean-gradstudies/future/academicprograms/AcceleratedMasters.html.
Program Length
Students are expected to complete the program requirements in five terms.
Other Information
Supervisory Committee
A supervisory committee consists of the student's supervisor, at least one other computing science faculty member, and others (typically faculty) as appropriate. The choice of supervisor should be made by mutual consent of the graduate student and faculty member based on commonality of research interests. The student and supervisor should consult on the remainder of the committee members.
Transfer from MSc to PhD Program
Students who are enrolled in the MSc program may apply to transfer to the doctor of philosophy (PhD) program after two terms and normally before the seventh term. Students must have a CGPA of 3.5 or above, completed 75% of the required master's course work and evidence must be provided that the student is capable of undertaking substantial original research.
Thesis Option
Students in the thesis option will demonstrate depth of knowledge in their research area through a thesis defence based on their independent work. Students should consult with their supervisory committee members, and formulate and submit a written thesis proposal for approval no later than the third term. Thesis students register in CMPT 898 during the terms in which they are conducting thesis research.
Project Option
Students in the project option will choose an area of specialization and submit a project report. Project topics may include a comprehensive survey of the literature of some computing science related research areas; implementation and evaluation of existing techniques/algorithms; development of interesting software/hardware applications. Project students register in CMPT 897 during the terms in which they are conducting project work. The project is examined as a thesis and will need to be submitted to the library as per Graduate General Regulation 1.10.4.
Course Work
The courses used to satisfy the breadth requirements must include either CMPT 705 or 710, unless the student already has credit for one of these courses (or equivalent) from a previous degree as determined by the graduate program breadth committee.
Only two special topics courses may be used toward satisfaction of breadth requirements, except with permission of the graduate program breadth committee.
Any 700 division course used to satisfy the breadth requirement may be waived and replaced by an 800 division course. Students must produce convincing evidence to the graduate program committee that they have completed a comparable course or have comparable training in industry.
Academic Requirements within the Graduate General Regulations
All graduate students must satisfy the academic requirements that are specified in the Graduate General Regulations, as well as the specific requirements for the program in which they are enrolled.