Last updated: January 22, 2008 @ 12:59PM

Computer Science

Master of Science in Computer Science

Ian Brindle
Faculty of Mathematics and Science

Associate Dean
Greg Finn
Faculty of Mathematics and Science

Graduate Faculty

Ivo Düntsch (Computer Science), Frank Fueten (Earth Sciences), Brian Ross (Computer Science), Thomas Wolf (Mathematics)

Associate Professors
Jerzy Barchanski (Computer Science), Sheridan Houghten (Computer Science), David Hughes (Computer Science), David McCarthy (Computer Science), Beatrice Ombuki-Berman (Computer Science), Ke Qiu (Computer Science), Jon Radue (Computer Science), Michael Winter (Computer Science), Vladimir Wojcik (Computer Science)

Assistant Professor
Omar Kihel (Mathematics)

Adjunct Professors
Günther Gediga (Institut für Evaluation & Marktanalysen Jeggen, Germany and Psychologisches Institut IV Universität Münster, Münster, Germany)

Department Chair
David Hughes

Graduate Program Director
Betty Ombuki

Administrative Assistant
Donna Phelps
905-688-5550, extension 3513
Mackenzie Chown J314

Program Description
The Department of Computer Science offers a program leading to the Master of Science (MSc) degree. Graduate research topics may be conducted in the broad areas of computational logic and algebra, data mining, evolutionary computation, artificial intelligence, algorithms, parallelism, and combinatorics. Please see the department web page for a listing of faculty and their specific research interests (

Admission Requirements
Successful completion of an Honours Bachelor's degree, or equivalent, in Computer Science, with a minimum of high B (75) average. In some circumstances, exceptional applicants with an Honours Bachelor's degree in a related discipline (e.g. mathematics, computer engineering) who have met the minimum high B (75) average, and have a demonstrated proficiency in fundamental computer science topics (see list below), may be considered. Qualifying courses may be required. 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.

The Graduate Admissions Committee will review all applications and recommend admission for a limited number of suitable candidates.

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.

Part-time study is available.

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 Masters degree may be required to complete additional preparatory courses in consultation with their supervisor, before commencing with their regular graduate courses.

Degree Requirements
For full-time students, the MSc is a six term or two year program. Every MSc candidate must prepare and defend a thesis, which demonstrates a capacity for independent work of high scientific calibre. A supervisory graduate committee will guide the student in all aspects of the student's graduate program. Students normally take four half-credit courses in the first year. Courses are selected in consultation with their assigned supervisor.

Candidates with an Honours degree in computer science or who have completed prescribed qualifying courses, require a minimum of one year of residency and satisfactory completion of the program, which must include the Master's thesis COSC 5F90, and four 5(alpha)00 or above level half credits, or three such half credits and one COSC 4(alpha)00 or above level half credit with the approval of the graduate committee. At most one of the graduate level courses can be a reading course. All candidates are required to present seminars on their background research and thesis topics as part of the COSC 5F90 course, and attend all the seminars of fellow graduate students and departmental seminars.

A campus-wide fiber optic network links all of the university's academic computing facilities. The department's computers form an integral part of this resource. All faculty and graduate students are provided with an account on the departmental server. Most computers on campus can be accessed from microcomputers in any of the laboratories.

Brock is also a full member of the SHARCnet consortium with access to all its high performance clusters of powerful workstations.

In addition to three servers, the department also maintains several PC based labs and UNIX workstations for teaching and research.

Course Descriptions
Note: Not all courses are offered in every session. Students must have their course selections approved by the Graduate Program Director each term. Refer to the Timetable for scheduling information:
MSc Thesis
The preparation and defence of a thesis demonstrating the candidate's ability for independent and original research.

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.

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.

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.

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.

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.

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.

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.

Evolutionary Computation
Study of evolutionary computation techniques and applications. Focus on genetic algorithms, evolutionary strategies and related sub-areas including ant colony systems and swarm intelligence. How these paradigms exploit biological processes in nature to solve problems. Exposure to a wide range of practical applications including scheduling, optimization, logistics, evolving neural networks, classification and image analysis.

Directed Reading in Computer Science
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 5V90-5V99
Selected Topics in Computer Science
Various advanced topics in computer science offered by faculty members.