Program Requirements
The B.Eng.; Minor in Applied Artificial Intelligence, open to all engineering students, is designed to provide the foundation for applications of AI techniques in various fields of interest.
Students must complete 7 courses as follows. Up to three courses can be double counted with the major.
Complementary Courses (22-25)
Group A
3 credits from the following:
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COMP 250 Introduction to Computer Science (3 credits) *
Overview
Computer Science (Sci) : Mathematical tools (binary numbers, induction,recurrence relations, asymptotic complexity,establishing correctness of programs). Datastructures (arrays, stacks, queues, linked lists,trees, binary trees, binary search trees, heaps,hash tables). Recursive and non-recursivealgorithms (searching and sorting, tree andgraph traversal). Abstract data types. Objectoriented programming in Java (classes andobjects, interfaces, inheritance). Selected topics.
Terms: Fall 2024, Winter 2025
Instructors: Alberini, Giulia (Fall) Alberini, Giulia (Winter)
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ECSE 250 Fundamentals of Software Development (3 credits) *
Overview
Electrical Engineering : Software development practices in the context of object-oriented programming. Elementary data structures such as lists, stacks and trees. Recursive and non-recursive algorithms: searching and sorting, tree and graph traversal. Asymptotic notation: Big O. Introduction to tools and practices employed in commercial software development.
Terms: Fall 2024, Winter 2025
Instructors: Lin, Hsiu-Chin (Fall) Wei, Lili (Winter)
Prerequisite: COMP 202
(3-2-4)
* COMP 250 and ECSE 250 cannot both be taken.
Group B
4 credits from the following:
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COMP 551 Applied Machine Learning (4 credits) *
Overview
Computer Science (Sci) : Selected topics in machine learning and data mining, including clustering, neural networks, support vector machines, decision trees. Methods include feature selection and dimensionality reduction, error estimation and empirical validation, algorithm design and parallelization, and handling of large data sets. Emphasis on good methods and practices for deployment of real systems.
Terms: Fall 2024, Winter 2025
Instructors: Prémont-Schwarz, Isabeau; Rabbany, Reihaneh (Fall) Li, Yue (Winter)
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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 2024, Winter 2025
Instructors: Armanfard, Narges (Fall) Armanfard, Narges (Winter)
* ECSE 551 and COMP 551 cannot both be taken
Group C
3 credits from the following:
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ECSE 343 Numerical Methods in Engineering (3 credits)
Overview
Electrical Engineering : Number representation and numerical error. Symbolic vs. numerical computation. Curve fitting and interpolation. Numerical differentiation and integration. Optimization. Data science pipelines and data-driven approaches. Preliminary machine learning. Solutions of systems of linear equations and nonlinear equations. Solutions of ordinary and partial differential equations. Applications in engineering, physical simulation, CAD, machine learning and digital media.
Terms: Winter 2025
Instructors: Khazaka, Roni (Winter)
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MATH 223 Linear Algebra (3 credits)
Overview
Mathematics & Statistics (Sci) : Review of matrix algebra, determinants and systems of linear equations. Vector spaces, linear operators and their matrix representations, orthogonality. Eigenvalues and eigenvectors, diagonalization of Hermitian matrices. Applications.
Terms: Fall 2024, Winter 2025
Instructors: Elaidi, Shereen; Bellemare, Hugues (Fall) Macdonald, Jeremy (Winter)
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MATH 247 Honours Applied Linear Algebra (3 credits)
Overview
Mathematics & Statistics (Sci) : Matrix algebra, determinants, systems of linear equations. Abstract vector spaces, inner product spaces, Fourier series. Linear transformations and their matrix representations. Eigenvalues and eigenvectors, diagonalizable and defective matrices, positive definite and semidefinite matrices. Quadratic and Hermitian forms, generalized eigenvalue problems, simultaneous reduction of quadratic forms. Applications.
Terms: Winter 2025
Instructors: Hoheisel, Tim (Winter)
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MATH 271 Linear Algebra and Partial Differential Equations (3 credits)
Overview
Mathematics & Statistics (Sci) : Applied Linear Algebra. Linear Systems of Ordinary Differential Equations. Power Series Solutions. Partial Differential Equations. Sturm-Liouville Theory and Applications. Fourier Transforms.
Terms: Fall 2024
Instructors: Roth, Charles (Fall)
Group D
3 credits from the following:
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AEMA 310 Statistical Methods 1 (3 credits)
Overview
Mathematics (Agric&Envir Sci) : Measures of central tendency and dispersion; binomial and Poisson distributions; normal, chi-square, Student's t and Fisher-Snedecor F distributions; estimation and hypothesis testing; simple linear regression and correlation; analysis of variance for simple experimental designs.
Terms: Fall 2024, Winter 2025
Instructors: Dutilleul, Pierre R L (Fall) Dhiman, Jaskaran (Winter)
Two 1.5-hour lectures and one 2-hour lab
Please note that credit will be given for only one introductory statistics course. Consult your academic advisor.
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CIVE 302 Probabilistic Systems (3 credits)
Overview
Civil Engineering : An introduction to probability and statistics with applications to Civil Engineering design. Descriptive statistics, common probability models, statistical estimation, regression and correlation, acceptance sampling.
Terms: Winter 2025
Instructors: Chouinard, Luc E (Winter)
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ECSE 205 Probability and Statistics for Engineers (3 credits)
Overview
Electrical Engineering : Probability: basic probability model, conditional probability, Bayes rule, random variables and vectors, distribution and density functions, common distributions in engineering, expectation, moments, independence, laws of large numbers, central limit theorem. Statistics: descriptive measures of engineering data, sampling distributions, estimation of mean and variance, confidence intervals, hypothesis testing, linear regression.
Terms: Fall 2024, Winter 2025
Instructors: Radhakrishnan, Sindhu (Fall) Radhakrishnan, Sindhu (Winter)
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MATH 203 Principles of Statistics 1 (3 credits)
Overview
Mathematics & Statistics (Sci) : Examples of statistical data and the use of graphical means to summarize the data. Basic distributions arising in the natural and behavioural sciences. The logical meaning of a test of significance and a confidence interval. Tests of significance and confidence intervals in the one and two sample setting (means, variances and proportions).
Terms: Fall 2024, Winter 2025
Instructors: Stephens, David; Correa, Jose Andres (Fall) Sajjad, Alia (Winter)
No calculus prerequisites
Restriction: This course is intended for students in all disciplines. For extensive course restrictions covering statistics courses see Section 3.6.1 of the Arts and of the Science sections of the calendar regarding course overlaps.
You may not be able to receive credit for this course and other statistic courses. Be sure to check the Course Overlap section under Faculty Degree Requirements in the Arts or Science section of the Calendar. Students should consult for information regarding transfer credits for this course.
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MATH 323 Probability (3 credits)
Overview
Mathematics & Statistics (Sci) : Sample space, events, conditional probability, independence of events, Bayes' Theorem. Basic combinatorial probability, random variables, discrete and continuous univariate and multivariate distributions. Independence of random variables. Inequalities, weak law of large numbers, central limit theorem.
Terms: Fall 2024, Winter 2025
Instructors: Sajjad, Alia (Fall) Nadarajah, Tharshanna (Winter)
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MECH 262 Statistics and Measurement Laboratory (3 credits)
Overview
Mechanical Engineering : Introduction to probability: conditional probability, binomial and Poisson distributions, random variables, laws of large numbers. Statistical analysis associated with measurements; regression and correlation. Basic experimental laboratory techniques, including the measurement of strain, pressure, force, position, and temperature.
Terms: Fall 2024, Winter 2025
Instructors: Nedic, Jovan (Fall) Nedic, Jovan (Winter)
(3-2-4)
Corequisite: MATH 263
Restriction: Open to U1 students or higher.
-
MIME 209 Mathematical Applications (3 credits)
Overview
Mining & Materials Engineering : Introduction to stochastic modelling of mining and metallurgical engineering processes. Description and analysis of data distributions observed in mineral engineering applications. Modelling with linear regression analysis. Taylor series application to error and uncertainty propagation. Metallurgical mass balance adjustments.
Terms: Winter 2025
Instructors: Amegadzie, Mark (Winter)
(3-2-4)
Group E
9-12 credits from the following:
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COMP 370 Introduction to Data
Science (3 credits)
Overview
Computer Science (Sci) : Comprehensive introduction to the data science process. Orientation to the use and configuration of core data science toolkits, data collection and annotation fundamentals, principles of responsible data science, the use of quantitative tools in data science, and presentation of data science findings.
Terms: Fall 2024
Instructors: Ruths, Derek (Fall)
-
COMP 417 Introduction Robotics and Intelligent Systems (3 credits)
Overview
Computer Science (Sci) : This course considers issues relevant to the design of robotic and of intelligent systems. How can robots move and interact. Robotic hardware systems. Kinematics and inverse kinematics. Sensors, sensor data interpretation and sensor fusion. Path planning. Configuration spaces. Position estimation. Intelligent systems. Spatial mapping. Multi-agent systems. Applications.
Terms: This course is not scheduled for the 2024-2025 academic year.
Instructors: There are no professors associated with this course for the 2024-2025 academic year.
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COMP 424 Artificial Intelligence (3 credits) ***
Overview
Computer Science (Sci) : Introduction to search methods. Knowledge representation using logic and probability. Planning and decision making under uncertainty. Introduction to machine learning.
Terms: Fall 2024
Instructors: Meger, David; Farnadi, Golnoosh (Fall)
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COMP 445 Computational Linguistics (3 credits)
Overview
Computer Science (Sci) : Introduction to foundational ideas in computational linguistics and natural language processing. Topics include formal language theory, probability theory, estimation and inference, and recursively defined models of language structure. Emphasis on both the mathematical foundations of the field as well as how to use these tools to understand human language.
Terms: This course is not scheduled for the 2024-2025 academic year.
Instructors: There are no professors associated with this course for the 2024-2025 academic year.
Prerequisite(s): COMP 250 and MATH 240, or permission of instructor.
Restriction: Not open to students who have taken or are taking LING 445.
Students who are taking or have taken both COMP 330 and COMP 424 are advised to take COMP 550 in place of COMP 445/LING 445.
This is a double-prefix course and is identical in content with LING 445.
Some background in linguistics at the level of LING 201 is desirable, though not critical.
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COMP 549 Brain-Inspired Artificial Intelligence (3 credits)
Overview
Computer Science (Sci) : 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.
Terms: Winter 2025
Instructors: Richards, Blake (Winter)
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COMP 550 Natural Language Processing (3 credits)
Overview
Computer Science (Sci) : An introduction to the computational modelling of natural language, including algorithms, formalisms, and applications. Computational morphology, language modelling, syntactic parsing, lexical and compositional semantics, and discourse analysis. Selected applications such as automatic summarization, machine translation, and speech processing. Machine learning techniques for natural language processing.
Terms: Fall 2024
Instructors: Cheung, Jackie; Adelani, David Ifeoluwa (Fall)
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COMP 562 Theory of Machine Learning (4 credits)
Overview
Computer Science (Sci) : Concentration inequalities, PAC model, VC dimension, Rademacher complexity, convex optimization, gradient descent, boosting, kernels, support vector machines, regression and learning bounds. Further topics selected from: Gaussian processes, online learning, regret bounds, basic neural network theory.
Terms: This course is not scheduled for the 2024-2025 academic year.
Instructors: There are no professors associated with this course for the 2024-2025 academic year.
Prerequisites: MATH 462 or COMP 451 or (COMP 551, MATH 222, MATH 223 and MATH 324) or ECSE 551.
Restrictions: Not open to students who have taken or are taking MATH 562. Not open to students who have taken COMP 599 when the topic was "Statistical Learning Theory" or "Mathematical Topics for Machine Learning". Not open to students who have taken COMP 598 when the topic was "Mathematical Foundations of Machine Learning".
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COMP 565 Machine Learning in Genomics and Healthcare (4 credits)
Overview
Computer Science (Sci) : 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.
Terms: Fall 2024
Instructors: Li, Yue (Fall)
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COMP 579 Reinforcement Learning (4 credits)
Overview
Computer Science (Sci) : 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.
Terms: Winter 2025
Instructors: Precup, Doina; Prémont-Schwarz, Isabeau (Winter)
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COMP 588 Probabilistic Graphical Models (4 credits)
Overview
Computer Science (Sci) : 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.
Terms: Winter 2025
Instructors: Ravanbakhsh, Siamak (Winter)
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ECSE 415 Introduction to Computer Vision (3 credits)
Overview
Electrical Engineering : An introduction to the automated processing, analysis, and understanding of image data. Topics include image formation and acquisition, design of image features, image segmentation, stereo and motion correspondence matching techniques, feature clustering, regression and classification for object recognition, industrial and consumer applications, and computer vision software tools.
Terms: Fall 2024, Winter 2025
Instructors: Clark, James J (Fall) Arbel, Tal (Winter)
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ECSE 446 Realistic Image Synthesis (3 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.
Terms: Fall 2024
Instructors: Nowrouzezahrai, Derek (Fall)
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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 2025
Instructors: Radhakrishnan, Sindhu (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 2024
Instructors: Cooperstock, Jeremy (Fall)
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ECSE 544 Computational Photography (4 credits)
Overview
Electrical Engineering : 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.
Terms: Winter 2025
Instructors: Clark, James J (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 2025
Instructors: Emad, Amin (Winter)
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ECSE 554 Applied Robotics
(4 credits)
Overview
Electrical Engineering : 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.
Terms: Fall 2024
Instructors: Lin, Hsiu-Chin (Fall)
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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 2024
Instructors: Emad, Amin (Fall)
3-0-9
Restrictions: Permission of Instructor.
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ECSE 557 Introduction to Ethics of Intelligent Systems (3 credits)
Overview
Electrical Engineering : 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.
Terms: Fall 2024
Instructors: Moon, AJung (Fall)
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MECH 559 Engineering Systems Optimization (3 credits)
Overview
Mechanical Engineering : 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.
Terms: Fall 2024
Instructors: Kokkolaras, Michael (Fall)
*** COMP 424 and ECSE 526 cannot both be taken.
Or any 400 or 500 level special topics courses in the area of artificial intelligence with the approval of the Electrical and Computer Engineering department.
