Electrical Engineering (Non-Thesis): Applied Artificial Intelligence (M.Eng.) (45 credits)
Offered by: Electrical & Computer Engr (Faculty of Engineering)
Degree: Master of Engineering
Program credit weight: 45
Program Description
The Master of Engineering in Electrical Engineering; Non-Thesis-Applied Artificial Intelligence is a professional program of 45 credits. The program provides the foundation for applications of Artificial Intelligence (AI) techniques and experience building an AI system in various fields of interest. The program may be completed on a part-time basis.
Note: For information about Fall 2025 and Winter 2026 course offerings, please check back on May 8, 2025. Until then, the "Terms offered" field will appear blank for most courses while the class schedule is being finalized.
Required Courses (14 credits)
| Course | Title | Credits |
|---|---|---|
| ECSE 551 | Machine Learning for Engineers. | 4 |
Machine Learning for Engineers. Terms offered: this course is not currently offered. 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. | ||
| ECSE 552 | Deep Learning. | 4 |
Deep Learning. Terms offered: this course is not currently offered. 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. | ||
| ECSE 679D1 | Project in Applied Artificial Intelligence. | 3 |
Project in Applied Artificial Intelligence. Terms offered: this course is not currently offered. A project on a topic related to an application of Artificial Intelligence. | ||
| ECSE 679D2 | Project in Applied Artificial Intelligence. | 3 |
Project in Applied Artificial Intelligence. Terms offered: this course is not currently offered. See ECSE 679D1 for course description. | ||
Complementary Courses
(18-24 credits)
Group A: Artificial Intelligence Focused
6-8 credits from the following:
| Course | Title | Credits |
|---|---|---|
| ECSE 526 | Artificial Intelligence. | 3 |
Artificial Intelligence. Terms offered: this course is not currently offered. 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. | ||
| ECSE 555 | Advanced Topics in Artificial Intelligence. | 4 |
Advanced Topics in Artificial Intelligence. Terms offered: this course is not currently offered. Selected topics in areas of artificial intelligence that are of current research interest. | ||
| ECSE 556 | Machine Learning in Network Biology. | 4 |
Machine Learning in Network Biology. Terms offered: this course is not currently offered. 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. | ||
| ECSE 557 | Introduction to Ethics of Intelligent Systems. | 3 |
Introduction to Ethics of Intelligent Systems. Terms offered: this course is not currently offered. Ethics and social issues related to AI and robotic systems. Consideration for normative values (e.g., fairness) in the design. Ethics principles, data and privacy issues, ethics challenges in interaction and interface design. | ||
| ECSE 626 | Statistical Computer Vision. | 4 |
Statistical Computer Vision. Terms offered: this course is not currently offered. 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. | ||
| ECSE 683 | Topics in Vision and Robotics. | 4 |
Topics in Vision and Robotics. Terms offered: this course is not currently offered. Special topics in vision and robotics. | ||
Group B: Mathematical Foundations of Artificial Intelligence
3-4 credits from the following:
| Course | Title | Credits |
|---|---|---|
| COMP 540 | Matrix Computations. | 4 |
Matrix Computations. Terms offered: this course is not currently offered. Designing and programming reliable numerical algorithms. Stability of algorithms and condition of problems. Reliable and efficient algorithms for solution of equations, linear least squares problems, the singular value decomposition, the eigenproblem and related problems. Perturbation analysis of problems. Algorithms for structured matrices. | ||
| ECSE 500 | Mathematical Foundations of Systems. | 3 |
Mathematical Foundations of Systems. Terms offered: this course is not currently offered. Basic set theories and algebraic structures, linear spaces, linear mappings, topological and metric spaces, separable spaces, continuity, compactness, Lebesque measure on Euclidean spaces, measurability, Banach spaces, Hilbert spaces, linear bounded operators in Banach spaces, dual spaces, adjoint operators, the Orthogonal Projection Theorem, properties of the Fourier series, convergence in probability. | ||
| ECSE 501 | Linear Systems. | 3 |
Linear Systems. Terms offered: this course is not currently offered. Mathematical models of linear systems, fundamental solution and transition matrices, non-homogeneous linear equations, controllability and observability of linear systems, reachable subspaces, Cayley-Hamilton's Theorem, Kalman's controllability and observability rank conditions, minimal realizations, frequency response, invariant subspaces, finite and infinite horizon linear regulator problems, uniform, exponential, and input-output stability, the Lyapunov equation. | ||
| ECSE 507 | Optimization and Optimal Control. | 3 |
Optimization and Optimal Control. Terms offered: this course is not currently offered. 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. | ||
| ECSE 509 | Probability and Random Signals 2. | 3 |
Probability and Random Signals 2. Terms offered: this course is not currently offered. Multivariate Gaussian distributions; finite-dimensional mean-square estimation (multivariate case); principal components; introduction to random processes; weak stationarity: correlation functions, spectra, linear processing and estimation; Poisson processes and Markov chains: state processes, invariant distributions; stochastic simulation. | ||
| ECSE 543 | Numerical Methods in Electrical Engineering. | 3 |
Numerical Methods in Electrical Engineering. Terms offered: this course is not currently offered. DC resistor networks and sparse matrix methods. Nonlinear electric and magnetic circuits: curve-fitting; the Newton-Raphson method. Finite elements for electrostatics. Transient analysis of circuits: systems of Ordinary differential equations; stiff equations. Transient analysis of induced currents. Solution of algebraic eigenvalue problems. Scattering of electromagnetic waves: the boundary element method; numerical integration. | ||
| ECSE 621 | Statistic Detection and Estimation. | 4 |
Statistic Detection and Estimation. Terms offered: this course is not currently offered. Statistical detection and estimation lies at the intersection of telecommunications, signal processing and mathematical statistics. Subjects include: hypothesis testing (Neyman-Pearson, Bayes, minimax, nuisance parameters, composite hypotheses, generalized likelihood), estimation theory (maximum-likelihood, maximum aposteriory probability, linear estimation, Cramer-Rao bounds). | ||
Group C: Applications of Artificial Intelligence
9-12 credits from the following:
| Course | Title | Credits |
|---|---|---|
| COMP 545 | Natural Language Understanding with Deep Learning . | 4 |
Natural Language Understanding with Deep Learning . Terms offered: this course is not currently offered. Neural network-based methods for natural language understanding (NLU) and computational semantics. Continuous representations for words, phrases, sentences, and discourse, and their connection to formal semantics. Practical and ethical considerations in applications such as text classification, question answering, and conversational assistants. | ||
| COMP 549 | Brain-Inspired Artificial Intelligence. | 3 |
Brain-Inspired Artificial Intelligence. Terms offered: this course is not currently offered. Overview of the influence of neuroscience and psychology on Artificial Intelligence (AI). Historical topics: perceptrons, the PDP framework, Hopfield nets, Boltzmann and Helmholtz machines, and the behaviourist origins of reinforcement learning. Modern topics: deep learning, attention, memory and consciousness. Emphasis on understanding the interdisciplinary foundations of modern AI. | ||
| COMP 558 | Fundamentals of Computer Vision. | 4 |
Fundamentals of Computer Vision. Terms offered: this course is not currently offered. Image filtering, edge detection, image features and histograms, image segmentation, image motion and tracking, projective geometry, camera calibration, homographies, epipolar geometry and stereo, point clouds and 3D registration. Applications in computer graphics and robotics. | ||
| COMP 565 | Machine Learning in Genomics and Healthcare. | 4 |
Machine Learning in Genomics and Healthcare. Terms offered: this course is not currently offered. Linear models in statistical genetics, causal inference, single-cell genomics, multi-omic learning, electronic health record mining. Applications of machine learning techniques: linear regression, latent factor models, variational Bayesian inference, neural networks, model interpretation. | ||
| COMP 579 | Reinforcement Learning. | 4 |
Reinforcement Learning. Terms offered: this course is not currently offered. Bandit algorithms, finite Markov decision processes, dynamic programming, Monte-Carlo Methods, temporal-difference learning, bootstrapping, planning, approximation methods, on versus off policy learning, policy gradient methods temporal abstraction and inverse reinforcement learning. | ||
| COMP 585 | Intelligent Software Systems . | 4 |
Intelligent Software Systems . Terms offered: this course is not currently offered. Practical aspects of building software systems with machine learning components: requirements, design, delivery, quality assessment, and collaboration. Consideration of a user-centered mindset in development; integration of design and development considerations relevant to artificial intelligence, such as security, privacy, and fairness. | ||
| COMP 588 | Probabilistic Graphical Models. | 4 |
Probabilistic Graphical Models. Terms offered: this course is not currently offered. Representation, inference and learning with graphical models; directed and undirected graphical models; exact inference; approximate inference using deterministic optimization based methods, stochastic sampling based methods; learning with complete and partial observations. | ||
| COMP 685 | Machine Learning Applied to Climate Change. | 4 |
Machine Learning Applied to Climate Change. Terms offered: this course is not currently offered. Applications of machine learning in fighting climate change, including use cases in electricity systems, buildings, transportation, agriculture and other land use, disaster response, and other areas. Review of recent research literature, with emphasis on when machine learning is relevant and helpful, and how to go about scoping, developing, and deploying a project so that it has the intended impact. | ||
| ECSE 506 | Stochastic Control and Decision Theory. | 3 |
Stochastic Control and Decision Theory. Terms offered: this course is not currently offered. Modelling of stochastic control systems, controlled Markov processes, dynamic programming, imperfect and delayed observations, linear quadratic and Gaussian (LQG) systems, team theory, information structures, static and dynamic teams, dynamic programming for teams,multi-armed bandits. | ||
| ECSE 508 | Multi-Agent Systems. | 3 |
Multi-Agent Systems. Terms offered: this course is not currently offered. 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. | ||
| ECSE 541 | Design of Multiprocessor Systems-on-Chip. | 3 |
Design of Multiprocessor Systems-on-Chip. Terms offered: this course is not currently offered. Modelling, design, evaluation, and optimization of multiprocessor systems-on-chips (MPSoCs). Introduction to system-level modelling of MPSoC architecture; system performance, power, and lifetime modelling; fault and defect tolerance; automatic general and heuristic design space exploration and design optimization; resource allocation, application mapping, and task scheduling. | ||
| ECSE 544 | Computational Photography. | 4 |
Computational Photography. Terms offered: this course is not currently offered. An overview of techniques and theory underlying computational photography. Topics include: radiometry and photometry; lenses and image formation; electronic image sensing; colour processing; lightfield cameras; image deblurring; super-resolution methods; image denoising; flash photography; image matting and compositing; high dynamic range imaging and tone mapping; image retargeting; image stitching. | ||
| ECSE 546 | Advanced Image Synthesis. | 4 |
Advanced Image Synthesis. Terms offered: this course is not currently offered. 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. | ||
| ECSE 554 | Applied Robotics . | 4 |
Applied Robotics . Terms offered: this course is not currently offered. The approach and the challenges in the key components of manipulators and locomotors: representations, kinematics, dynamics, rigid-body chains, redundant systems, underactuated systems, control, planning, and perception. Practical aspects of robotics: collisions, integrating sensory feedback, and development of real-time software. | ||
| MECH 559 | Engineering Systems Optimization. | 3 |
Engineering Systems Optimization. Terms offered: this course is not currently offered. Introduction to systems-oriented engineering design optimization. Emphasis on i) understanding and representing engineering systems and their structure, ii) obtaining, developing, and managing adequate computational (physics- and data-based) models for their analysis, iii) constructing appropriate design models for their synthesis, and iv) applying suitable algorithms for their numerical optimization while accounting for systems integration issues. Advanced topics such as coordination of distributed problems and non-deterministic design optimization methods. | ||
Elective Courses
(7-13 credits)
7-13 credits at the 500 or 600 level (excluding ECSE 691 to ECSE 697)
* No more that 16 credits in total may be outside the Department. With the exception of courses in the Complementary Courses list, non-departmental courses require Departmental Approval. In exceptional circumstances and with proper justification, students may be permitted to take more than 16 credits of non-Departmental courses; approval from the Graduate Program Director or delegate is required.