Note: This is the 2023鈥�2024 eCalendar. Update the year in your browser's URL bar for the most recent version of this page, or .
Program Requirements
The M.Sc. in Computer Science; Non-Thesis offers an in depth study of advanced topics in computer science, mainly through course-based work. The program includes the possibility to complete a short research project or to conduct an internship for practical experience.
Required Courses (2 credits)
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COMP 602 Computer Science Seminar 1 (1 credit)
Overview
Computer Science (Sci) : Exposure to ongoing research directions in computer science through regular attendance of the research colloquium organized by the School of Computer Science.
Terms: Fall 2023
Instructors: Kry, Paul; Meger, David (Fall)
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COMP 603 Computer Science Seminar 2 (1 credit)
Overview
Computer Science (Sci) : Exposure to ongoing research directions in computer science through regular attendance of the research colloquium organized by the School of Computer Science.
Terms: Winter 2024
Instructors: Kry, Paul; Meger, David (Winter)
Complementary Courses (43 credits)
Choose either: project courses and course work; or internship and course work; or all course work.
Research Project
0-15 credits from:
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COMP 693 Research Project 1 (3 credits)
Overview
Computer Science (Sci) : Ongoing research pertaining to project.
Terms: Fall 2023, Winter 2024, Summer 2024
Instructors: There are no professors associated with this course for the 2023-2024 academic year.
Restriction: Computer Science students
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COMP 694 Research Project 2 (6 credits)
Overview
Computer Science (Sci) : Ongoing research pertaining to project.
Terms: Fall 2023, Winter 2024, Summer 2024
Instructors: There are no professors associated with this course for the 2023-2024 academic year.
Restriction: Computer Science students
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COMP 695 Research Project 3 (6 credits)
Overview
Computer Science (Sci) : Ongoing research pertaining to project.
Terms: Fall 2023, Winter 2024, Summer 2024
Instructors: There are no professors associated with this course for the 2023-2024 academic year.
Restriction: Computer Science students
Internship
0-15 credits from:
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COMP 689 Internship in Computer Science (15 credits)
Overview
Computer Science (Sci) : Four month internship in a company or organization, to give experience with industrial practices in computer science, data science or software engineering.
Terms: Winter 2024, Summer 2024
Instructors: There are no professors associated with this course for the 2023-2024 academic year.
Student must have taken at least four complementary courses within the program before taking the internship course.
The student will work with both an industrial and academic supervisor to ensure alignment both with the company or organization needs and with the academic goals, namely suitability for the M.Sc. level.
Course Work
28-43 credits of lecture- or seminar-based COMP courses at the 500 level or higher.
The following courses outside o the School of Computer Science may count towards the complementary courses, subject to approval by an academic adviser.
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ECSE 507 Optimization and Optimal Control (3 credits)
Overview
Electrical Engineering : General introduction to optimization methods including steepest descent, conjugate gradient, Newton algorithms. Generalized matrix inverses and the least squared error problem. Introduction to constrained optimality; convexity and duality; interior point methods. Introduction to dynamic optimization; existence theory, relaxed controls, the Pontryagin Maximum Principle. Sufficiency of the Maximum Principle.
Terms: Winter 2024
Instructors: Flynn, Joshua (Winter)
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ECSE 508 Multi-Agent Systems (3 credits)
Overview
Electrical Engineering : Introduction to game theory, strategic games, extensive form games with perfect and imperfect information, repeated games and folk theorems, cooperative game theory, introduction to mechanism design, markets and market equilibrium, pricing and resource allocation, application in telecommunication networks, applications in communication networks, stochastic games.
Terms: This course is not scheduled for the 2023-2024 academic year.
Instructors: There are no professors associated with this course for the 2023-2024 academic year.
(3-0-6)
Prerequisite(s): ECSE 205 or equivalent.
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ECSE 516 Nonlinear and Hybrid Control Systems (3 credits)
Overview
Electrical Engineering : Examples of hybrid control systems (HCS). Review of nonlinear system state, controllability, observability, stability. HCS specified via ODEs and automata. Continuous and discrete states and dynamics; controlled and autonomous discrete state switching. HCS stability via Lyapunov theory and LaSalle Invariance Principle. Hybrid Maximum Principle and Hybrid Dynamic Programming; computational algorithms.
Terms: This course is not scheduled for the 2023-2024 academic year.
Instructors: There are no professors associated with this course for the 2023-2024 academic year.
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ECSE 518 Telecommunication Network Analysis (3 credits)
Overview
Electrical Engineering : Mathematical modeling and analysis techniques for the control and management of modern networks. Introduction to queuing networks; birth/death processes; routing optimization and fairness; multi-commodity network flow; traffic modeling; effective bandwidth and network calculus; performance modeling.
Terms: This course is not scheduled for the 2023-2024 academic year.
Instructors: There are no professors associated with this course for the 2023-2024 academic year.
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ECSE 523 Speech Communications (3 credits)
Overview
Electrical Engineering : Articulatory and acoustic descriptions of speech production, speech production models, speech perception, digital processing of speech signals, vocodors using formant, linear predictive and cepstral techniques, overview of automatic speech recognition systems, speech synthesis systems and speaker verification systems.
Terms: Winter 2024
Instructors: O'Shaughnessy, Douglas (Winter)
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ECSE 526 Artificial Intelligence (3 credits)
Overview
Electrical Engineering : Design principles of autonomous agents, agent architectures, machine learning, neural networks, genetic algorithms, and multi-agent collaboration. The course includes a term project that consists of designing and implementing software agents that collaborate and compete in a simulated environment.
Terms: Fall 2023
Instructors: Cooperstock, Jeremy (Fall)
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ECSE 539 Advanced Software Language Engineering (4 credits)
Overview
Electrical Engineering : Practical and theoretical knowledge for developing software languages and models; foundations for model-based software development; topics include principles of model-driven engineering; concern-driven development; intentional, structural, and behavioral models as well as configuration models; constraints; language engineering; domain-specific languages; metamodelling; model transformations; models of computation; model analyses; and modeling tools.
Terms: Winter 2024
Instructors: Mussbacher, Gunter (Winter)
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ECSE 542 Human Computer Interaction (4 credits)
Overview
Electrical Engineering : Design, development, and evaluation of human-computer interfaces, with emphasis on usability, interaction paradigms, computer-mediated human activities, and implications to society. These issues are studied from a number of perspectives including that of the engineer and end-user. A team-based project applies knowledge and skills to the full life cycle of an interactive human-computer interface.
Terms: Fall 2023
Instructors: Cooperstock, Jeremy (Fall)
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ECSE 546 Advanced Image Synthesis (4 credits)
Overview
Electrical Engineering : Introduction to mathematical models of light transport and the numerical techniques used to generate realistic images in computer graphics. Offline (i.e., raytracing) and interactive (i.e., shader-based) techniques. Group project addressing important applied research problems.
Terms: Fall 2023
Instructors: Nowrouzezahrai, Derek (Fall)
(3-2-7)
Restrictions: For graduate students in Electrical and Computer Engineering and undergraduate Honours Electrical Engineering students.
Not open to students who have taken or are taking ECSE 446.
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ECSE 551 Machine Learning for Engineers (4 credits)
Overview
Electrical Engineering : Introduction to machine learning: challenges and fundamental concepts. Supervised learning: Regression and Classification. Unsupervised learning. Curse of dimensionality: dimension reduction and feature selection. Error estimation and empirical validation. Emphasis on good methods and practices for deployment of real systems.
Terms: Fall 2023, Winter 2024
Instructors: Armanfard, Narges (Fall) Armanfard, Narges (Winter)
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ECSE 552 Deep Learning (4 credits)
Overview
Electrical Engineering : Overview of mathematical background and basics of machine learning, deep feedforward networks, regularization for deep learning, optimization for training deep learning models, convolutional neural networks, recurrent and recursive neural networks, practical considerations,applications of deep learning, recent models and architectures in deep learning.
Terms: Winter 2024
Instructors: Emad, Amin (Winter)
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ECSE 556 Machine Learning in Network Biology (4 credits)
Overview
Electrical Engineering : Basics of machine learning; basics of molecular biology; network-guided machine learning in systems biology; network-guided bioinformatics analysis; analysis of biological networks; network module identification; global and local network alignment; construction of biological networks.
Terms: Fall 2023
Instructors: Emad, Amin (Fall)
3-0-9
Restrictions: Permission of Instructor.
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ECSE 570 Automatic Speech Recognition (3 credits)
Overview
Electrical Engineering : Acoustic phonetics and signal representations. Pattern classification, stochastic modelling, language modelling and search algorithms as applied to speech recognition. Techniques for robustness, integration of speech recognition with other user interface modalities, and the role of automatic speech recognition in speech understanding.
Terms: This course is not scheduled for the 2023-2024 academic year.
Instructors: There are no professors associated with this course for the 2023-2024 academic year.
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ECSE 626 Statistical Computer Vision (4 credits)
Overview
Electrical Engineering : An overview of statistical and machine learning techniques as applied to computer vision problems, including: stereo vision, motion estimation, object and face recognition, image registration and segmentation. Topics include regularization, probabilistic inference, information theory, Gaussian Mixture Models, Markov-Chain Monte Carlo methods, importance sampling, Markov random fields, principal and independent components analysis, probabilistic deep learning methods including variational models, Bayesian deep learning.
Terms: This course is not scheduled for the 2023-2024 academic year.
Instructors: There are no professors associated with this course for the 2023-2024 academic year.
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MATH 523 Generalized Linear Models (4 credits)
Overview
Mathematics & Statistics (Sci) : Exponential families, link functions. Inference and parameter estimation for generalized linear models; model selection using analysis of deviance. Residuals. Contingency table analysis, logistic regression, multinomial regression, Poisson regression, log-linear models. Multinomial models. Overdispersion and Quasilikelihood. Applications to experimental and observational data.
Terms: Winter 2024
Instructors: Steele, Russell (Winter)
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MATH 524 Nonparametric Statistics (4 credits)
Overview
Mathematics & Statistics (Sci) : Distribution free procedures for 2-sample problem: Wilcoxon rank sum, Siegel-Tukey, Smirnov tests. Shift model: power and estimation. Single sample procedures: Sign, Wilcoxon signed rank tests. Nonparametric ANOVA: Kruskal-Wallis, Friedman tests. Association: Spearman's rank correlation, Kendall's tau. Goodness of fit: Pearson's chi-square, likelihood ratio, Kolmogorov-Smirnov tests. Statistical software packages used.
Terms: This course is not scheduled for the 2023-2024 academic year.
Instructors: There are no professors associated with this course for the 2023-2024 academic year.
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MATH 533 Regression and Analysis of Variance (4 credits)
Overview
Mathematics & Statistics (Sci) : Multivariate normal and chi-squared distributions; quadratic forms. Multiple linear regression estimators and their properties. General linear hypothesis tests. Prediction and confidence intervals. Asymptotic properties of least squares estimators. Weighted least squares. Variable selection and regularization. Selected advanced topics in regression. Applications to experimental and observational data.
Terms: Fall 2023
Instructors: Dagdoug, Mehdi (Fall)
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MATH 559 Bayesian Theory and Methods (4 credits)
Overview
Mathematics & Statistics (Sci) : Subjective probability, Bayesian statistical inference and decision making, de Finetti鈥檚 representation. Bayesian parametric methods, optimal decisions, conjugate models, methods of prior specification and elicitation, approximation methods. Hierarchical models. Computational approaches to inference, Markov chain Monte Carlo methods, Metropolis鈥擧astings. Nonparametric Bayesian inference.
Terms: Fall 2023
Instructors: Stephens, David (Fall)
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MATH 563 Honours Convex Optimization
(4 credits)
Overview
Mathematics & Statistics (Sci) : Honours level introduction to convex analysis and convex optimization: Convex sets and functions, subdifferential calculus, conjugate functions, Fenchel duality, proximal calculus. Subgradient methods, proximal-based methods. Conditional gradient method, ADMM. Applications including data classification, network-flow problems, image processing, convex feasibility problems, DC optimization, sparse optimization, and compressed sensing.
Terms: Winter 2024
Instructors: Paquette, Courtney (Winter)
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MATH 578 Numerical Analysis 1 (4 credits)
Overview
Mathematics & Statistics (Sci) : Development, analysis and effective use of numerical methods to solve problems arising in applications. Topics include direct and iterative methods for the solution of linear equations (including preconditioning), eigenvalue problems, interpolation, approximation, quadrature, solution of nonlinear systems.
Terms: Fall 2023
Instructors: Lessard, Jean-Philippe (Fall)
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MATH 680 Computation Intensive Statistics (4 credits)
Overview
Mathematics & Statistics (Sci) : General introduction to computational methods in statistics; optimization methods; EM algorithm; random number generation and simulations; bootstrap, jackknife, cross-validation, resampling and permutation; Monte Carlo methods: Markov chain Monte Carlo and sequential Monte Carlo; computation in the R language.
Terms: Fall 2023
Instructors: Steele, Russell (Fall)
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MECH 513 Control Systems (3 credits)
Overview
Mechanical Engineering : State-space modelling and related linear algebra. Controllability and observability of linear time-invariant systems and corresponding tests, system realizations. Stability: Bounded-Input-Bounded-Output (BIBO), internal, Lyapunov. Linear state feedback control: pole placement and root locus design methods, linear quadratic regulator. State observers: full- and reduced-order designs, separation principle, Linear Quadratic Gaussian (LQG) design. Introduction to optimal control and optimal state estimation.
Terms: Winter 2024
Instructors: Forbes, James (Winter)
Faculty of Science—2023-2024 (last updated Sep. 12, 2023) (disclaimer)
