Applied Artificial Intelligence Minor (B.Eng.) (25 credits)
Offered by: Electrical & Computer Engr (Faculty of Engineering)
Degree: Bachelor of Engineering
Program credit weight: 25
Program Description
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.
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.
Complementary Courses (22-25)
Group A
3 credits from the following:
| Course | Title | Credits |
|---|---|---|
| COMP 250 | Introduction to Computer Science. 1 | 3 |
Introduction to Computer Science. Terms offered: this course is not currently offered. 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. | ||
| ECSE 250 | Fundamentals of Software Development. 1 | 3 |
Fundamentals of Software Development. Terms offered: this course is not currently offered. 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. | ||
- 1
COMP 250 Introduction to Computer Science. and ECSE 250 Fundamentals of Software Development. cannot both be taken.
Group B
4 credits from the following:
| Course | Title | Credits |
|---|---|---|
| COMP 551 | Applied Machine Learning. 1 | 4 |
Applied Machine Learning. Terms offered: this course is not currently offered. 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. | ||
| ECSE 551 | Machine Learning for Engineers. 1 | 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. | ||
- 1
ECSE 551 Machine Learning for Engineers. and COMP 551 Applied Machine Learning. cannot both be taken
Group C
3 credits from the following:
| Course | Title | Credits |
|---|---|---|
| ECSE 343 | Numerical Methods in Engineering. | 3 |
Numerical Methods in Engineering. Terms offered: this course is not currently offered. 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. | ||
| MATH 223 | Linear Algebra. | 3 |
Linear Algebra. Terms offered: this course is not currently offered. 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. | ||
| MATH 247 | Honours Applied Linear Algebra. | 3 |
Honours Applied Linear Algebra. Terms offered: this course is not currently offered. 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. | ||
| MATH 271 | Linear Algebra and Partial Differential Equations. | 3 |
Linear Algebra and Partial Differential Equations. Terms offered: this course is not currently offered. Applied Linear Algebra. Linear Systems of Ordinary Differential Equations. Power Series Solutions. Partial Differential Equations. Sturm-Liouville Theory and Applications. Fourier Transforms. | ||
Group D
3 credits from the following:
| Course | Title | Credits |
|---|---|---|
| AEMA 310 | Statistical Methods 1. | 3 |
Statistical Methods 1. Terms offered: Fall 2025, Winter 2026 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. | ||
| CIVE 302 | Probabilistic Systems. | 3 |
Probabilistic Systems. Terms offered: this course is not currently offered. An introduction to probability and statistics with applications to Civil Engineering design. Descriptive statistics, common probability models, statistical estimation, regression and correlation, acceptance sampling. | ||
| ECSE 205 | Probability and Statistics for Engineers | 3 |
Probability and Statistics for Engineers Terms offered: Summer 2025 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. | ||
| MATH 203 | Principles of Statistics 1. | 3 |
Principles of Statistics 1. Terms offered: Summer 2025 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). | ||
| MATH 323 | Probability. | 3 |
Probability. Terms offered: Summer 2025 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. | ||
| MATH 262 | Intermediate Calculus. | 3 |
Intermediate Calculus. Terms offered: Summer 2025 Series and power series, including Taylor's theorem. Brief review of vector geometry. Vector functions and curves. Partial differentiation and differential calculus for vector valued functions. Unconstrained and constrained extremal problems. Multiple integrals including surface area and change of variables. | ||
| MIME 209 | Mathematical Applications. | 3 |
Mathematical Applications. Terms offered: this course is not currently offered. 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. | ||
Group E
9-12 credits from the following:
| Course | Title | Credits |
|---|---|---|
| COMP 417 | Introduction Robotics and Intelligent Systems. | 3 |
Introduction Robotics and Intelligent Systems. Terms offered: this course is not currently offered. 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. | ||
| COMP 424 | Artificial Intelligence. 1 | 3 |
Artificial Intelligence. Terms offered: this course is not currently offered. Introduction to search methods. Knowledge representation using logic and probability. Planning and decision making under uncertainty. Introduction to machine learning. | ||
| COMP 445 | Computational Linguistics. | 3 |
Computational Linguistics. Terms offered: this course is not currently offered. 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. | ||
| 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 562 | Theory of Machine Learning. | 4 |
Theory of Machine Learning. Terms offered: this course is not currently offered. 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. | ||
| 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 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. | ||
| ECSE 415 | Introduction to Computer Vision. | 3 |
Introduction to Computer Vision. Terms offered: this course is not currently offered. 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. | ||
| ECSE 446 | Realistic Image Synthesis. | 3 |
Realistic 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. | ||
| 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 526 | Artificial Intelligence. 1 | 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 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 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 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. | ||
| 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. | ||
| 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. | ||
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.