|
Master of Science in Computer Science Dean Peter Berg Faculty of Mathematics and Science Associate Dean Cheryl McCormick Faculty of Mathematics and Science Core Faculty Professors Sheridan Houghten, Beatrice Ombuki-Berman, Ke Qiu, Brian Ross, Michael Winter Associate Professor Robson De Grande Assistant Professors Renata Dividino, Ali Emami, Naser Ezzati-Jivan, Yifeng Li Professor Emeritus Ivo Düntsch Participating Graduate Faculty Professors Ping Liang (Biology), Shahryar Rahnamayan (Engineering), Thomas Wolf (Mathematics) Adjunct Professors Joseph Brown (Thompson Rivers University), Kyle Harrison (University of Newcastle), James Hughes (St. Francis Xavier University) Graduate Program Director Ke Qiu Graduate Administrative Coordinator Elena Genkin 905-688-5550, extension 3115 Mackenzie Chown D473 brocku.ca/mathematics-science/computer-science/programs/graduate/ |
||
 |
||
|
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, combinatorics, software engineering, bioinformatics, data science, natural language processing, networks, and security. Please see the department web page for a listing of faculty and their specific research interests (brocku.ca/mathematics-science/computer-science/programs/graduate/). The program offers two options: a thesis option (suitable for students planning doctoral studies or industry employment) and a project option (suitable for students planning industry employment). |
||
|
||
|
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. 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 and algorithms, 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. The Graduate Admissions Committee will review all applications and recommend admission for a limited number of candidates. Part-time study is available. |
||
|
||
|
The computer science MSc offers two options - a thesis option MSc and project option MSc. For full-time students in the thesis option, the MSc is normally 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. Degree requirements for the thesis option must include COSC 5F90 (Thesis) and four 5(alpha)00 or above level COSC half-credits, or three such COSC 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 may be a directed reading course. All candidates are required to present seminars on their background research and thesis topics as part of the COSC 5F90 thesis course, attend all the seminars of fellow graduate students and departmental seminars. For full-time students in the project option, the MSc is normally a four term or 16 month program. Every MSc candidate must take six 5(alpha)00 or above level COSC half-credits, and a 5F99 Project. The course requirements permit one COSC 4(alpha)00 or above credit. All candidates must prepare and submit a COSC 5F99 project, which demonstrates proficiency in applying concepts in computer science in a practical application. The project is typically completed in the final two terms of study. All MSc students can take one-half non-COSC credit, in consultation with their supervisor. Cross-discipline courses that are suitable as an elective for the project option include (but are not limited to): BIOL 5P06; GEOG 5P25; MATH 5P20, 5P21, 5P35, 5P36, 5P92; and MBAB 5P14, 5P17. The availability of these courses will vary in different years. Students should consider these and other suitable courses in consultation with the supervisor. |
||
|
||
|
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. |
||
|
||
|
Note that not all courses are offered in every session. Refer to the applicable timetable for details Students must 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. MSc Thesis Preparation and defence of a thesis demonstrating the candidate's ability for independent and original research. Directed Project Practical application of techniques & concepts in computer science under the consultation of an assigned supervisor. Coding Theory 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 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. Application of logic to various areas such as computability, programming languages, program specification and verification. Universal Algebra for Computer Science 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. 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. 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. Software Performance Engineering 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. Graph Data Mining Investigation of recent methods and algorithms for exploring and analyzing large graph datasets. Topics in graph data mining including graph classification, node classification, clustering and community detection, graph summarization, graph similarity, link analysis, and anomaly detection in graphs. Artificial Intelligence in Cybersecurity Introduction of the application of artificial intelligence in the field of cybersecurity. Focus on AI tools and techniques to automate cybersecurity operations, predict, detect, and identify cyber-threats. Topics include AI in malware detection, anomaly detection, and cyberattack prediction, with emphasis on cybersecurity. Genetic Programming 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 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. Directed Reading 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. Non-invasive Data Analysis 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. Probabilistic Graphical Models and Neural Generative Models 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. Advanced Machine Learning Fundamental and advanced concepts in machine learning. Basic concepts: classification, regression, kernel techniques, feature selection, inference, sampling techniques, Bayesian networks, and energy-based models. Advanced topics: representation learning, neural networks for images and sequences, graph neural networks, deep generative models, transfer learning, attention mechanisms. Computational Intelligence and Applications Nature inspired algorithms and their practical applications in optimization, learning and design. Topics include evolutionary computation, neural computation, swarm intelligence and principles of self-organization in social agent systems. Reinforcement Learning (also offered as COSC 4P83) Introduction to fundamental reinforcement learning concepts including multi-armed bandits, Markov decision processes, model-based and model-free methods (such as dynamic programming, Monte Carlo methods, and temporal-difference methods) for learning value and policy functions. Approximation solutions including deep reinforcement learning. Natural Language Processing Theoretical and methodological introduction to the computational modelling of natural language, including techniques and algorithms, formalisms, and applications. Fundamental topics underlying current Natural Language Processing technologies in detail, including but not limited to computational morphology, lexical and compositional semantics, syntactic parsing, language modelling, summarization, machine translation, and coreference resolution. Various machine learning techniques showing promising results on many Natural Language Processing benchmarks. Selected Topics in Computer Science Various advanced topics in computer science offered by faculty members. |
||
 |
||
2023-2024 Graduate Calendar
Last updated: March 23, 2023 @ 11:47AM