* Mandatory ancillary fees include recovery fees, UHIP, daycare fee, bus pass and engagement levy
Successful completion of four year Bachelor's degree, or equivalent, in Computer Science, with at least a minimum average of 75 (B). In some circumstances, exceptional applicants with four year Bachelor's degree in a related discipline (e.g. mathematics, computer engineering) who have met the minimum average of 75 (B), and have a demonstrated proficiency in fundamental computer science topics (see list below), may be considered. Such applicants may consider submitting a result from the Graduate Record Examination (GRE) subject test in computer science to strengthen their application. Agreement from a faculty advisor to supervise the student is also required for admission to the program.
Applicants are expected to have completed courses in the following areas: computer organization, operating systems, file structures and data management, principles of programming languages, data structures, software analysis and design, formal languages and automata, calculus, linear algebra, statistics and/or probability, discrete mathematics, and additional four upper level (third or fourth year) half courses in other topics in computer science. Candidates lacking sufficient background in the area of the intended Master's degree may be required to complete additional preparatory courses in consultation with their supervisor.
Those applicants holding a three or four year Bachelor's degree and who meet academic requirements of an overall B average may be asked to complete a qualifying term/year to upgrade their application. Completion of a qualifying term/year does not guarantee acceptance into the program
Master’s applicants who have not completed three or more years of post-secondary study, and doctoral applicants who have not completed two or more years of post-secondary study, at a Canadian institution or at an institution in one of the following exempt countries, will be required to provide proof of English language proficiency.
Applicants who are required to provide proof of English Language Proficiency must complete the English Language Proficiency (Self Report) form through their student portal account, as well as upload one of the accepted programs/tests listed to the right.
English Proficiency Tests (EPT)
TOEFL iBT, Minimum overall score of 80, with no subtest below 19
IELTS (Academic), Minimum overall score of 6.5, with no subtest below 5.5
CAEL, Minimum overall score of 60, with 60 in writing, and no other subtest below 50
CAEL CE (Computer Edition), Minimum overall score of 60, with 60 in writing, and no other subtest below 50
PTE Academics, Minimum overall score of 60, with no subtest below 60
Can Test for Scholars and Trainees, Minimum overall score of 4.5, with no subtest below 4.0
Language Schools
Brock’s Intensive English Language Program (IELP), Completion of Level 5
ESC (Language School Pathway), Completion of the UCTP
ILAC (Language School Pathway), Completion of University Pathway Level III (12 weeks)
ILSC (Language School Pathway), Completion of Level A2
CLLC (Language School Pathway), Completion of UPP Level 7
Three references are required.
A Computer Science Statement of Research Interest Form in which applicants indicate their career aspirations/plans, specific research interests (note that they must be selected from faculty research interests) and experience relevant to their interests.
Applicants are asked to indicate potential links between their own research interests and goals and the research interests of faculty participating in the graduate program. If a potential thesis supervisor has been contacted, he/ she must be identified in the statement of research interest. Although not required, it is strongly recommended that applicants specify their potential supervisors.
A Curriculum Vitae which should indicate the applicant’s education, employment, publication, teaching and research activities, and experience.
Brock’s master’s program in Computer Science is a research based MSc that consists of one year of course work. This is followed by the preparation of a thesis or project to obtain advanced knowledge in computer science and experience in scientific research. Research may be conducted in the broad areas of computational logic and algebra, data mining, evolutionary computation, artificial intelligence, algorithms, parallelism and combinatorics.
Our graduate program offers two options: thesis-based MSc and project-based MSc.
The thesis-based MSc is appropriate for students who may select a career in either industry or academia, with the possibility of continuing on to a PhD after graduation. This 2 year program consists of course work and thesis research. Full-time students normally take four half-credit graduate-level courses during the first year. Every MSc candidate must prepare and defend a thesis during their second year. This thesis research will demonstrate a capacity for independent work of high scientific calibre. Course selection and thesis research is done in consultation with the student's assigned supervisor.
In September 2017, a new project-based MSc will be available. This option may be of interest to students who wish to proceed directly to careers in industry after graduation, and do not wish to continue to PhD studies. Students must complete six half-credit courses during their first year. They must also complete a one-credit project under the direction of their faculty supervisor. The total duration of the project-based MSc is normally 16 months, which makes the option faster to complete than the thesis-based option.
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Course Descriptions,
Note that not all courses are offered in every session. Refer to the applicable timetable for details
Students must check to ensure that prerequisites are met. Students may be deregistered, at the request of the instructor, from any course for which prerequisites and/or restrictions have not been met.
COSC 5F90
MSc Thesis
The preparation and defence of a thesis demonstrating the candidate's ability for independent and original research.
COSC 5F99
Directed Project
A project that applies techniques and concepts in computer science towards a practical application. The topic is chosen, and work is performed, under the consultation of an assigned supervisor.
COSC 5P01
Coding Theory
The main concepts, problems and applications related to error-correcting codes. Different classes of codes and their properties. Emphasis on algorithms relating to codes, examination of algorithms for encoding and decoding, together with algorithms that may be used in computer searches for specific classes of codes.
COSC 5P02
Logic in Computer Science
A thorough introduction to mathematical logic, covering the following topics: propositional and first-order logic; soundness, completeness, and compactness of first-order logic; first-order theories; undecidability and Gödel's incompleteness theorem; and an introduction to other logics such as intuitionistic and modal logics. Furthermore, the course stresses the application of logic to various areas of computer science such as computability, programming languages, program specification and verification.
COSC 5P03
Universal Algebra for Computer Science
The study of the concepts and constructs of Universal Algebra, such as products, subalgebras, homomorphic images and congruences, term algebras, free algebras, its connections with Logic and Model Theory, decidability issues, lattices and relation algebras, and applications in Computer Science such as Type Theory, Specification, Complexity Theory, Uncertainty Management and others.
COSC 5P04
Parallel Algorithms
Introduction to parallel processing, various parallel computational models including both shared-memory and distributed-memory models, speed-up, cost, design and analysis of parallel algorithms and data structures for a variety of problems in searching, sorting, graph theory, computational geometry, strings, and numerical computation; brief introduction to parallel complexity.
COSC 5P05
Introduction to Lambda Calculus
Introduction to typed and untyped lambda calculi and their semantics. Syntax of the lambda calculus, conversion, fixed points, reduction, Church-Rosser theorem, representation of recursive functions, lambda models. Category theory, cartesian closed categories and categorical models of lambda calculus.
COSC 5P06
Principles of Mobile Cloud Computing
Fundamentals supporting Mobile Cloud Computing, including task offloading, connectivity of mobile networks, and remote storage. Study of the basics and recent research advancements on principles of mobile computing, distributed applications and services, Cloud computing and virtualization, management and use of resources offered by Cloud service providers, computation offloading and thin-client computing, and application scenarios and selected use cases.
COSC 5P07
Software Performance Engineering
The study of concepts, techniques, and metrics in software performance engineering before, during, and after software development. This covers performance practices throughout the software development life cycle, performance and scalability testing and principles of performance evaluation including instrumentation, profiling, measurement, and benchmarking.
COSC 5P71
Genetic Programming
The synthesis of computer programs using evolutionary computation. The study of different representations, including tree, linear, grammatical. Theoretical analyses, including the effects of operators, representations, fitness landscapes. Practical applications in problem solving, decision making, classification, computer vision, design.
COSC 5P72
Robot Control Architectures
Survey of approaches to control in single and multi-robot systems. Examination and study of different mobile robot control architectures, including deliberative, reactive and hybrid with focus on the issues of resolving the fundamental conflict between thinking and acting, i.e., high-level deliberation and real-time control. Other relevant topics including communication techniques in multirobot systems and safety characteristics of the studied control architectures.
COSC 5P73
Computer Vision and Visual Computer Learning
Introduction to computer vision and pattern classification. The problems of WHAT and WHERE. The issue of knowledge representation and performance. Knowledge consolidation models. The concept of recursive (i.e. evolutionary) computer learning. Visual learning. Guided learning from infallible and fallible experts. Autonomous learning and experimentation. Analysis of HPC architectures conducive to visual computer learning.
COSC 5P74
Evolutionary Computation
Study of basic concepts of evolutionary algorithms (EAs) from a theoretical and application viewpoint. This includes genetic algorithms, evolutionary strategies, genetic programming, problem representation, genetic operations, overall control, theory of EAs and various examples of important applications. Includes related bio-inspired sub-areas such as swarm intelligence, and evolutionary robotics.
COSC 5P75
Directed Reading
A reading course designed for the individual student and subject to final approval by the department graduate committee. Usually offered by the student's thesis supervisor but may also be offered by other faculty members after consultation with the supervisor.
COSC 5P76
Non-invasive Data Analysis
A study of data analysis using information from the given data based on the rough set data model. Includes an overview of the data modeling process, principles of probability, non-parametric significance testing, data filtering and discretization, model selection, rule validation, case studies.
COSC 5P77
Probabilistic Graphical Models and Neural Generative Models
An introduction to the theoretical foundations of generative models and their applications in prediction, knowledge discovery, and creative design. Foundations of probabilistic graphical models (PGMs), learning, and inference in traditional PGMs such as hidden Markov models, mixture models, and latent Dirichlet allocation. Undirected neural generative models (NGMs), including Markov random fields, and variants of Boltzmann machines. Directed NGMs, including Helmholtz machines and deep belief nets, variational autoencoders, and generative adversarial networks.
COSC 5V90-5V99
Selected Topics in Computer Science
Various advanced topics in computer science offered by faculty members,
PhD programs in Computer Science
Industry and government careers
Software engineering
Research and Development
Information technology
Artificial intelligence
Startup companies
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