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Minor Applied Artificial Intelligence (25 credits)

Offered by: Electrical & Computer Engr     Degree: Bachelor of Engineering

Program Requirements

** NEW PROGRAM **
(22-25 credits)

Students must complete 7 courses as follows. Up to three courses can be double counted with the major.

Required Course

  • COMP 250 Introduction to Computer Science (3 credits)

    Offered by: Computer Science (Faculty of Science)

    Overview

    Computer Science (Sci) : Mathematical tools (binary numbers, induction, recurrence relations, asymptotic complexity, establishing correctness of programs), Data structures (arrays, stacks, queues, linked lists, trees, binary trees, binary search trees, heaps, hash tables), Recursive and non-recursive algorithms (searching and sorting, tree and graph traversal). Abstract data types, inheritance. Selected topics.

    Terms: Fall 2020, Winter 2021

    Instructors: Alberini, Giulia (Fall) Alberini, Giulia (Winter)

    • 3 hours

    • Prerequisites: Familiarity with a high level programming language and CEGEP level Math.

    • Students with limited programming experience should take COMP 202 or equivalent before COMP 250. See COMP 202 Course Description for a list of topics.

Complementary Courses (19-22 credits)

Group A
4 credits from the following:

  • COMP 551 Applied Machine Learning (4 credits) *

    Offered by: Computer Science (Faculty of Science)

    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 2020, Winter 2021

    Instructors: Ravanbakhsh, Mohsen (Fall) Rabbany, Reihaneh (Winter)

    • Prerequisite(s): MATH 323 or ECSE 205 or ECSE 305 or equivalent

    • Restriction(s): Not open to students who have taken or are taking COMP 451.

    • Some background in Artificial Intelligence is recommended, e.g. COMP-424 or ECSE-526, but not required.

  • ECSE 551 Machine Learning for Engineers (4 credits) *

    Offered by: Electrical & Computer Engr (Faculty of Engineering)

    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 2020, Winter 2021

    Instructors: Armanfard, Narges (Fall) Armanfard, Narges (Winter)

* ECSE 551 and COMP 551 cannot both be taken

Group B
3 credits from the following:

  • ECSE 343 Numerical Methods in Engineering (3 credits)

    Offered by: Electrical & Computer Engr (Faculty of Engineering)

    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 2021

    Instructors: Khazaka, Roni (Winter)

  • MATH 223 Linear Algebra (3 credits)

    Offered by: Mathematics and Statistics (Faculty of Science)

    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 2020, Winter 2021

    Instructors: Pichot, Michael (Fall)

    • Fall and Winter

    • Prerequisite: MATH 133 or equivalent

    • Restriction: Not open to students in Mathematics programs nor to students who have taken or are taking MATH 236, MATH 247 or MATH 251. It is open to students in Faculty Programs

  • MATH 247 Honours Applied Linear Algebra (3 credits)

    Offered by: Mathematics and Statistics (Faculty of Science)

    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 2021

    Instructors: Hoheisel, Tim (Winter)

    • Winter

    • Prerequisite: MATH 133 or equivalent.

    • Restriction: Intended for Honours Physics and Engineering students

    • Restriction: Not open to students who have taken or are taking MATH 236, MATH 223 or MATH 251

  • MATH 271 Linear Algebra and Partial Differential Equations (3 credits)

    Offered by: Mathematics and Statistics (Faculty of Engineering)

    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 2020

    Instructors: Roth, Charles (Fall)

Group C
3 credits from the following:

  • AEMA 310 Statistical Methods 1 (3 credits)

    Offered by: Plant Science (Agricultural & Environmental Sciences)

    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 2020, Winter 2021

    Instructors: Dutilleul, Pierre R L; Hoyos-Villegas, Valerio (Fall) Dutilleul, Pierre R L; Hoyos-Villegas, Valerio (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.

  • CIVE 302 Probabilistic Systems (3 credits)

    Offered by: Civil Engineering (Faculty of Engineering)

    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 2021

    Instructors: Chouinard, Luc E (Winter)

    • (3-2-4)

    • Prerequisites: MATH 262, COMP 208 (a D grade is acceptable for prerequisite purposes)

  • ECSE 205 Probability and Statistics for Engineers (3 credits)

    Offered by: Electrical & Computer Engr (Faculty of Engineering)

    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 2020, Winter 2021

    Instructors: Leib, Harry (Fall) Leib, Harry (Winter)

    • Not open to students who have taken ECSE 305.

    • (3-2-4)

  • MATH 203 Principles of Statistics 1 (3 credits)

    Offered by: Mathematics and Statistics (Faculty of Science)

    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 2020, Winter 2021

    Instructors: Correa, Jose Andres; Sajjad, Alia (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 http://www.mcgill.ca/students/transfercredit for information regarding transfer credits for this course.

  • MATH 323 Probability (3 credits)

    Offered by: Mathematics and Statistics (Faculty of Science)

    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 2020, Winter 2021

    Instructors: Sajjad, Alia; Wolfson, David B (Fall) Wolfson, David B; Sajjad, Alia (Winter)

    • Prerequisites: MATH 141 or equivalent.

    • Restriction: Intended for students in Science, Engineering and related disciplines, who have had differential and integral calculus

    • Restriction: Not open to students who have taken or are taking MATH 356

  • MECH 262 Statistics and Measurement Laboratory (3 credits)

    Offered by: Mechanical Engineering (Faculty of Engineering)

    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 2020, Winter 2021

    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)

    Offered by: Mining & Materials Engineering (Faculty of Engineering)

    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 2021

    Instructors: Hasan, Mainul (Winter)

    • (3-2-4)

Group D
9-12 credits from the following:

  • COMP 579 Reinforcement Learning (4 credits)

    Offered by: Computer Science (Faculty of Science)

    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 2021

    Instructors: Precup, Doina (Winter)

    • Prerequisite: A university level course in machine learning such as COMP 451 or COMP 551. Background in calculus, linear algebra, probability at the level of MATH 222, MATH 223, MATH 323, respectively.

  • ECSE 415 Introduction to Computer Vision (3 credits)

    Offered by: Electrical & Computer Engr (Faculty of Engineering)

    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 2020, Winter 2021

    Instructors: Clark, James J (Fall) Arbel, Tal (Winter)

  • ECSE 446 Realistic Image Synthesis (3 credits)

    Offered by: Electrical & Computer Engr (Faculty of Engineering)

    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: This course is not scheduled for the 2020-2021 academic year.

    Instructors: There are no professors associated with this course for the 2020-2021 academic year.

  • ECSE 507 Optimization and Optimal Control (3 credits)

    Offered by: Electrical & Computer Engr (Faculty of Engineering)

    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 2021

    Instructors: There are no professors associated with this course for the 2020-2021 academic year.

  • ECSE 526 Artificial Intelligence (3 credits)

    Offered by: Electrical & Computer Engr (Faculty of Engineering)

    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 2020

    Instructors: Cooperstock, Jeremy (Fall)

  • ECSE 544 Computational Photography (4 credits)

    Offered by: Electrical & Computer Engr (Faculty of Engineering)

    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 2021

    Instructors: Clark, James J (Winter)

  • ECSE 552 Deep Learning (4 credits)

    Offered by: Electrical & Computer Engr (Faculty of Engineering)

    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 2021

    Instructors: Emad, Amin (Winter)

  • MECH 559 Engineering Systems Optimization (3 credits)

    Offered by: Mechanical Engineering (Faculty of Engineering)

    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: Winter 2021

    Instructors: Kokkolaras, Michael (Winter)

    • (3-0-6)

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.

Faculty of Engineering—2020-2021 (last updated Mar. 27, 2020) (disclaimer)
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