Please note:
To view the current Academic Calendar, go to www.sfu.ca/students/calendar.html.
Visual Computing
The master of visual computing engages students in developing deep knowledge and practical skills in the area of visual computing. The program trains visual data scientists and engineers who apply specialized knowledge in computer science to develop cutting-edge tools, stimulate product innovation, and explore new technology fronts in all commercial, engineering, and creative professions. Students take instructional and applied courses in a cohort, and complete work placement through ¶¡ÏãÔ°AV's co-op program, allowing them to tackle real-world scientific, engineering, and socio-economic problems and gain valuable project management experience while expanding their network of industry contacts. This full-time master's program is suitable for students with a strong aptitude in computer science, or other quantitative fields, such as engineering and mathematics.
¶¡ÏãÔ°AV Requirements
Applicants must satisfy the university admission requirements as stated in Graduate General Regulation 1.3 in the ¶¡ÏãÔ°AV Calendar. Applicants should normally have a bachelor's degree, or equivalent in computer science or a related field. Students who do not meet the minimum university requirements may be recommended as conditional or qualifying students as per Graduate General Regulation (GGR) 1.3.8 or 1.3.9.
For further information on conditional or qualifying admission requirements, please contact the Program Coordinator.
Program Requirements
This program consists of course work, and co-op or graduate project for a minimum of 30 graduate units. Students must maintain a minimum 3.0 CGPA throughout their graduate career.
Students complete all of
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.
| Section | Instructor | Day/Time | Location |
|---|---|---|---|
|
Steven Bergner |
Sep 4 – Oct 11, 2024: Tue, 10:30–11:20 a.m.
Oct 16 – Dec 3, 2024: Tue, 10:30–11:20 a.m. Sep 4 – Dec 3, 2024: Thu, 9:30–11:20 a.m. |
Burnaby Burnaby Burnaby |
Lab practices, combined with instructional offerings, for students to acquire the hands-on experience necessary for a successful career in Visual Computing in the information technology sector. Topics covered will include fundamental and prevalent problems from application domains in the fields of computer graphics, computer vision, human-computer interaction, medical image analysis, as well as visualization. Prerequisite: This course is only available to students enrolled in the master of visual computing program.
| Section | Instructor | Day/Time | Location |
|---|---|---|---|
|
Ali Mahdavi Amiri |
Sep 4 – Dec 3, 2024: Mon, 10:30 a.m.–1:20 p.m.
|
Burnaby |
|
| G101 |
Ali Mahdavi Amiri |
Sep 4 – Dec 3, 2024: Fri, 10:30 a.m.–1:20 p.m.
|
Burnaby |
Lab practices, combined with instructional offerings, for students to acquire the hands-on experience necessary for a successful career in Visual Computing in the information technology sector. Topics covered will include fundamental and prevalent problems from application domains in the fields of computer graphics, computer vision, human-computer interaction, medical image analysis, as well as visualization. Prerequisite: CMPT 742. This course is only available to students enrolled in the master of visual computing program.
Students will learn principles and techniques for processing various data types at real-world scale using distributed and cloud computing resources. Fundamentals of approximation and distributed algorithms will be covered. Handling of large-scale image and video datasets, massive graphs, as well as structured and unstructured text datasets will be studied. Designing and building robust software systems using multicore processors, processor accelerators (e.g., Graphics Processing Units) and cloud resources will be introduced.
and an additional nine units of graduate courses in computing science
and one of
This course is the first term of work experience in the School of Computing Science Co-operative Education Program for graduate students. Units of this course do not count towards computing science breadth requirements. Graded on a satisfactory/unsatisfactory basis. Prerequisite: 12 units of CMPT coursework at the 700-level or higher with a CGPA of at least 3.0. Department Consent is required for enrollment.
| Section | Instructor | Day/Time | Location |
|---|---|---|---|
|
Tanya Behrisch Cristina Eftenaru Shannon Danson |
Sep 4 – Oct 11, 2024: Mon, Tue, Wed, Thu, Fri, 8:00 a.m.–8:00 p.m.
Oct 16 – Dec 3, 2024: Mon, Tue, Wed, Thu, Fri, 8:00 a.m.–8:00 p.m. |
|
|
|
Tanya Behrisch Cristina Eftenaru Shannon Danson |
Sep 4 – Oct 11, 2024: Mon, Tue, Wed, Thu, Fri, 8:00 a.m.–8:00 p.m.
Oct 16 – Dec 3, 2024: Mon, Tue, Wed, Thu, Fri, 8:00 a.m.–8:00 p.m. |
|
|
|
Tanya Behrisch Cristina Eftenaru Shannon Danson |
Sep 4 – Oct 11, 2024: Mon, Tue, Wed, Thu, Fri, 8:00 a.m.–8:00 p.m.
Oct 16 – Dec 3, 2024: Mon, Tue, Wed, Thu, Fri, 8:00 a.m.–8:00 p.m. |
|
|
|
Tanya Behrisch Cristina Eftenaru Shannon Danson |
Sep 4 – Oct 11, 2024: Mon, Tue, Wed, Thu, Fri, 8:00 a.m.–8:00 p.m.
Oct 16 – Dec 3, 2024: Mon, Tue, Wed, Thu, Fri, 8:00 a.m.–8:00 p.m. |
|
|
|
Tanya Behrisch Cristina Eftenaru Shannon Danson |
Sep 4 – Oct 11, 2024: Mon, Tue, Wed, Thu, Fri, 8:00 a.m.–8:00 p.m.
Oct 16 – Dec 3, 2024: Mon, Tue, Wed, Thu, Fri, 8:00 a.m.–8:00 p.m. |
|
|
|
Tanya Behrisch Cristina Eftenaru Shannon Danson |
Sep 4 – Oct 11, 2024: Mon, Tue, Wed, Thu, Fri, 8:00 a.m.–8:00 p.m.
Oct 16 – Dec 3, 2024: Mon, Tue, Wed, Thu, Fri, 8:00 a.m.–8:00 p.m. |
|
|
|
Tanya Behrisch Cristina Eftenaru Shannon Danson |
Sep 4 – Oct 11, 2024: Mon, Tue, Wed, Thu, Fri, 8:00 a.m.–8:00 p.m.
Oct 16 – Dec 3, 2024: Mon, Tue, Wed, Thu, Fri, 8:00 a.m.–8:00 p.m. |
|
|
|
Tanya Behrisch Cristina Eftenaru Shannon Danson |
Sep 4 – Oct 11, 2024: Mon, Tue, Wed, Thu, Fri, 8:00 a.m.–8:00 p.m.
Oct 16 – Dec 3, 2024: Mon, Tue, Wed, Thu, Fri, 8:00 a.m.–8:00 p.m. |
|
|
|
Tanya Behrisch Cristina Eftenaru Shannon Danson |
Sep 4 – Oct 11, 2024: Mon, Tue, Wed, Thu, Fri, 8:00 a.m.–8:00 p.m.
Oct 16 – Dec 3, 2024: Mon, Tue, Wed, Thu, Fri, 8:00 a.m.–8:00 p.m. |
|
Program Length
Students are expected to complete the program requirements in four terms.
Other Information
Co-op
A co-op is an integral part of this program. However, it is offered on a competitive basis.
All students are required to apply for a co-op. With assistance from the co-op coordinator for this program, students will be expected to find a suitable industry partner. Students may complete one or two terms of co-op. The latter option is in place to satisfy requests from our industrial partners for continuity and to carry out a large-scale project. Students are required to enroll in at least one of the program courses in the term following their co-op.
In the event that a student is unable to secure a co-op during the summer term, they will be required to go on academic break since no courses will be offered. The student will be able to apply for a co-op in the subsequent term or, if unsuccessful, will be required to undertake additional course work. In consultation with the program director, the student may complete a graduate project in their final term to fulfil program requirements.
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.