Major Computer Science - Artificial Intelligence (63 credits)

Offered by: Computer Science     Degree: Bachelor of Science

Program Requirements

The B.Sc.; Major in Computer Science: Artificial Intelligence focuses on topics that relate to artificial intelligence and machine learning, including both foundations and applications. Students may complete this program with a minimum of 63 credits or a maximum of 68 credits.

Required Courses (39-42 credits)

* Students who have sufficient knowledge in a programming language do not need to take COMP 202.

  • COMP 202 Foundations of Programming (3 credits) *

    Offered by: Computer Science (Faculty of Science)

    Overview

    Computer Science (Sci) : Introduction to computer programming in a high level language: variables, expressions, primitive types, methods, conditionals, loops. Introduction to algorithms, data structures (arrays, strings), modular software design, libraries, file input/output, debugging, exception handling. Selected topics.

    Terms: Fall 2024, Winter 2025

    Instructors: M'hiri, Faten (Fall) M'hiri, Faten (Winter)

    • 3 hours

    • Prerequisite: a CEGEP level mathematics course

    • Restrictions: Not open to students who have taken or are taking COMP 204, COMP 208, or GEOG 333; not open to students who have taken or are taking COMP 206 or COMP 250.

    • COMP 202 is intended as a general introductory course, while COMP 204 is intended for students in life sciences, and COMP 208 is intended for students in physical sciences and engineering.

  • COMP 206 Introduction to Software Systems (3 credits)

    Offered by: Computer Science (Faculty of Science)

    Overview

    Computer Science (Sci) : Comprehensive overview of programming in C, use of system calls and libraries, debugging and testing of code; use of developmental tools like make, version control systems.

    Terms: Fall 2024, Winter 2025

    Instructors: Errington, Jacob (Fall) Vybihal, Joseph P (Winter)

  • 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). 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)

  • COMP 251 Algorithms and Data Structures (3 credits)

    Offered by: Computer Science (Faculty of Science)

    Overview

    Computer Science (Sci) : Introduction to algorithm design and analysis. Graph algorithms, greedy algorithms, data structures, dynamic programming, maximum flows.

    Terms: Fall 2024, Winter 2025

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

    • 3 hours

    • Prerequisites: COMP 250; MATH 235 or MATH 240

    • COMP 251 uses basic counting techniques (permutations and combinations) that are covered in MATH 240 but not in MATH 235. These techniques will be reviewed for the benefit of MATH 235 students.

    • Restrictions: Not open to students who have taken or are taking COMP 252.

  • COMP 273 Introduction to Computer Systems (3 credits)

    Offered by: Computer Science (Faculty of Science)

    Overview

    Computer Science (Sci) : Number representations, combinational and sequential digital circuits, MIPS instructions and architecture datapath and control, caches, virtual memory, interrupts and exceptions, pipelining.

    Terms: Fall 2024, Winter 2025

    Instructors: Elsaadawy, Mona (Fall) Kry, Paul (Winter)

  • COMP 302 Programming Languages and Paradigms (3 credits)

    Offered by: Computer Science (Faculty of Science)

    Overview

    Computer Science (Sci) : Programming language design issues and programming paradigms. Binding and scoping, parameter passing, lambda abstraction, data abstraction, type checking. Functional and logic programming.

    Terms: Fall 2024, Winter 2025

    Instructors: Pientka, Brigitte (Fall) Errington, Jacob (Winter)

  • COMP 303 Software Design (3 credits)

    Offered by: Computer Science (Faculty of Science)

    Overview

    Computer Science (Sci) : Principles, mechanisms, techniques, and tools for object-oriented software design and its implementation, including encapsulation, design patterns, and unit testing.

    Terms: Fall 2024

    Instructors: Robillard, Martin (Fall)

  • COMP 424 Artificial Intelligence (3 credits)

    Offered by: Computer Science (Faculty of Science)

    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)

  • MATH 222 Calculus 3 (3 credits)

    Offered by: Mathematics and Statistics (Faculty of Science)

    Overview

    Mathematics & Statistics (Sci) : Taylor series, Taylor's theorem in one and several variables. Review of vector geometry. Partial differentiation, directional derivative. Extreme of functions of 2 or 3 variables. Parametric curves and arc length. Polar and spherical coordinates. Multiple integrals.

    Terms: Fall 2024, Winter 2025

    Instructors: Pym, Brent (Fall)

  • 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 2024, Winter 2025

    Instructors: Macdonald, Jeremy (Winter)

    • 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 240 Discrete Structures (3 credits)

    Offered by: Mathematics and Statistics (Faculty of Science)

    Overview

    Mathematics & Statistics (Sci) : Introduction to discrete mathematics and applications. Logical reasoning and methods of proof. Elementary number theory and cryptography: prime numbers, modular equations, RSA encryption. Combinatorics: basic enumeration, combinatorial methods, recurrence equations. Graph theory: trees, cycles, planar graphs.

    Terms: Fall 2024, Winter 2025

    Instructors: Macdonald, Jeremy (Fall) Fortier, Jérôme (Winter)

    • Fall and Winter

    • Corequisite: MATH 133.

    • Restriction: For students in any Computer Science, Computer Engineering, or Software Engineering programs. Others only with the instructor's permission. Not open to students who have taken or are taking MATH 235.

  • 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 2024, Winter 2025

    Instructors: Sajjad, Alia (Fall) Nadarajah, Tharshanna (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

  • MATH 324 Statistics (3 credits)

    Offered by: Mathematics and Statistics (Faculty of Science)

    Overview

    Mathematics & Statistics (Sci) : Sampling distributions, point and interval estimation, hypothesis testing, analysis of variance, contingency tables, nonparametric inference, regression, Bayesian inference.

    Terms: Fall 2024, Winter 2025

    Instructors: Nadarajah, Tharshanna (Fall) Asgharian, Masoud (Winter)

    • Fall and Winter

    • Prerequisite: MATH 323 or equivalent

    • Restriction: Not open to students who have taken or are taking MATH 357

    • 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.

Complementary Courses (24-26 credits)

Group A:
6 credits selected from:

  • COMP 330 Theory of Computation (3 credits)

    Offered by: Computer Science (Faculty of Science)

    Overview

    Computer Science (Sci) : Finite automata, regular languages, context-free languages, push-down automata, models of computation, computability theory, undecidability, reduction techniques.

    Terms: Fall 2024, Winter 2025

    Instructors: Waldispuhl, Jérôme (Fall) Crépeau, Claude (Winter)

  • COMP 350 Numerical Computing (3 credits)

    Offered by: Computer Science (Faculty of Science)

    Overview

    Computer Science (Sci) : Computer representation of numbers, IEEE Standard for Floating Point Representation, computer arithmetic and rounding errors. Numerical stability. Matrix computations and software systems. Polynomial interpolation. Least-squares approximation. Iterative methods for solving a nonlinear equation. Discretization methods for integration and differential equations.

    Terms: Fall 2024

    Instructors: Chang, Xiao-Wen (Fall)

  • COMP 360 Algorithm Design (3 credits)

    Offered by: Computer Science (Faculty of Science)

    Overview

    Computer Science (Sci) : Advanced algorithm design and analysis. Linear programming, complexity and NP-completeness, advanced algorithmic techniques.

    Terms: Fall 2024, Winter 2025

    Instructors: Robere, Robert (Fall) Hatami, Hamed (Winter)

Group B:
3 credits selected from:

  • COMP 310 Operating Systems (3 credits)

    Offered by: Computer Science (Faculty of Science)

    Overview

    Computer Science (Sci) : Control and scheduling of large information processing systems. Operating system software - resource allocation, dispatching, processors, access methods, job control languages, main storage management. Batch processing, multiprogramming, multiprocessing, time sharing.

    Terms: Fall 2024, Winter 2025

    Instructors: Maheswaran, Muthucumaru (Fall) Liu, Xue (Winter)

  • COMP 421 Database Systems (3 credits)

    Offered by: Computer Science (Faculty of Science)

    Overview

    Computer Science (Sci) : Database Design: conceptual design of databases (e.g., entity-relationship model), relational data model, functional dependencies. Database Manipulation: relational algebra, SQL, database application programming, triggers, access control. Database Implementation: transactions, concurrency control, recovery, query execution and query optimization.

    Terms: Winter 2025

    Instructors: Kemme, Bettina; Elsaadawy, Mona (Winter)

Group C:
3 or 4 credits selected from:

  • COMP 451 Fundamentals of Machine Learning (3 credits)

    Offered by: Computer Science (Faculty of Science)

    Overview

    Computer Science (Sci) : Introduction to the computational, statistical and mathematical foundations of machine learning. Algorithms for both supervised learning and unsupervised learning. Maximum likelihood estimation, neural networks, and regularization.

    Terms: Fall 2024

    Instructors: Ravanbakhsh, Siamak (Fall)

  • 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 2024, Winter 2025

    Instructors: Prémont-Schwarz, Isabeau; Rabbany, Reihaneh (Fall) Li, Yue (Winter)

Group D:
3 credits selected from:

  • COMP 345 From Natural Language to Data Science (3 credits)

    Offered by: Computer Science (Faculty of Science)

    Overview

    Computer Science (Sci) : ntroduction to language data science, including theoretical approaches and practical skills. Processing, searching, and querying text data; making sense of large corpora; modelling and interpreting psycholinguistic and historical language data; building models of sequences of words; computing similarity between languages; information retrieval and extraction; question answering; and ethics.

    Terms: Winter 2025

    Instructors: Reddy, Siva (Winter)

    • Prerequisites: COMP 250, and MATH 240; or permission of the instructor.

    • Restriction: Not open to students who have taken or are taking LING 345.

  • COMP 370 Introduction to Data Science (3 credits)

    Offered by: Computer Science (Faculty of Science)

    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)

    • Prerequisites: COMP 206 and COMP 250

    • Restrictions: Not open to students who have taken COMP 598 when the topic was "Introduction to Data Science" or "Data Science".

Group E:
3 or 4 credits selected from:

  • COMP 417 Introduction Robotics and Intelligent Systems (3 credits)

    Offered by: Computer Science (Faculty of Science)

    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.

  • COMP 445 Computational Linguistics (3 credits)

    Offered by: Computer Science (Faculty of Science)

    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.

  • COMP 511 Network Science (4 credits)

    Offered by: Computer Science (Faculty of Science)

    Overview

    Computer Science (Sci) : Selected topics in Network Science, Graph Mining and Graph Learning, including patterns in real world networks, ranking and similarity measures for graphs, graph clustering and community mining techniques, and node classification and link prediction methods.

    Terms: Winter 2025

    Instructors: Rabbany, Reihaneh (Winter)

  • COMP 514 Applied Robotics (4 credits)

    Offered by: Computer Science (Faculty of Science)

    Overview

    Computer Science (Sci) : The approach and the challenges in the key components of manipulators and locomotors: representations, kinematics, dynamics, rigid-body chains, redundant systems, under-actuated systems, control, planning, and perception. Practical aspects of robotics: collisions, integrating sensory feedback, and real-time software development.

    Terms: Fall 2024

    Instructors: Lin, Hsiu-Chin (Fall)

    • Prerequisites: MATH 223, MATH 323, COMP 206, and COMP 250, or equivalents.

    • Restrictions: Not open to students who have taken COMP 597 when the topic was "Applied Robotics".

    • Students should be comfortable with C++ (such as from COMP 322) and a Unix-like programming environment.

  • COMP 545 Natural Language Understanding with Deep Learning (4 credits)

    Offered by: Computer Science (Faculty of Science)

    Overview

    Computer Science (Sci) : 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.

    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.

  • COMP 549 Brain-Inspired Artificial Intelligence (3 credits)

    Offered by: Computer Science (Faculty of Science)

    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)

    • Prerequisites: MATH 222, MATH 223, and MATH 323; or equivalents.

    • Restrictions: Not open to students who have taken COMP 596 when the topic was "Brain-Inspired Artificial Intelligence".

  • COMP 550 Natural Language Processing (3 credits)

    Offered by: Computer Science (Faculty of Science)

    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 (Fall)

  • COMP 558 Fundamentals of Computer Vision (4 credits)

    Offered by: Computer Science (Faculty of Science)

    Overview

    Computer Science (Sci) : 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.

    Terms: Fall 2024

    Instructors: Siddiqi, Kaleem (Fall)

  • COMP 562 Theory of Machine Learning (4 credits)

    Offered by: Computer Science (Faculty of Science)

    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: Winter 2025

    Instructors: Oberman, Adam (Winter)

    • 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".

  • COMP 565 Machine Learning in Genomics and Healthcare (4 credits)

    Offered by: Computer Science (Faculty of Science)

    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)

  • 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 2025

    Instructors: Precup, Doina; Prémont-Schwarz, Isabeau (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.

  • COMP 585 Intelligent Software Systems (4 credits)

    Offered by: Computer Science (Faculty of Science)

    Overview

    Computer Science (Sci) : 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.

    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.

  • 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 2025

    Instructors: Emad, Amin (Winter)

  • ECSE 557 Introduction to Ethics of Intelligent Systems (3 credits)

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

    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)

Group F:
6 credits of COMP courses at the 300 level or above (except COMP 396).

Faculty of Science—2024-2025 (last updated Apr. 3, 2024) (disclaimer)
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