![important](/study/2021-2022/files/study.2021-2022/exclamation-point-small.png)
Note: This is the 2017–2018 eCalendar. Update the year in your browser's URL bar for the most recent version of this page, or .
Note: This is the 2017–2018 eCalendar. Update the year in your browser's URL bar for the most recent version of this page, or .
Students may complete this program with a minimum of 72 credits or a maximum of 75 credits depending if they are exempt from taking COMP 202.
Honours students must maintain a CGPA of at least 3.00 during their studies and at graduation.
* Students who have sufficient knowledge in a programming language do not need to take COMP 202.
** Students take either MATH 340 or MATH 350.
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 2017, Winter 2018, Summer 2018
Instructors: Becerra Romero, David; Alberini, Giulia (Fall) Oakes, Bentley; Alberini, Giulia (Winter) Alberini, Giulia (Summer)
3 hours
Prerequisite: a CEGEP level mathematics course
Restrictions: COMP 202 and COMP 208 cannot both be taken for credit. COMP 202 is intended as a general introductory course, while COMP 208 is intended for students interested in scientific computation. COMP 202 cannot be taken for credit with or after COMP 250
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 2017, Winter 2018
Instructors: Vybihal, Joseph P (Fall) Meger, David (Winter)
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 2017, Winter 2018
Instructors: Langer, Michael (Fall) Gonzalez Oliver, Carlos; Waldispuhl, Jérôme (Winter)
Computer Science (Sci) : The design and analysis of data structures and algorithms. The description of various computational problems and the algorithms that can be used to solve them, along with their associated data structures. Proving the correctness of algorithms and determining their computational complexity.
Terms: Winter 2018
Instructors: Devroye, Luc P (Winter)
3 hours
Restrictions: Open only to students registered in following programs: Honours in Computer Science, Joint Honours in Mathematics and Computer Science, Honours in Applied Mathematics, Honours in Mathematics. Not open to students who have taken or are taking COMP 251.
Note: COMP 252 can be used instead of COMP 251 to satisfy prerequisites.
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 2017, Winter 2018
Instructors: Siddiqi, Kaleem (Fall) Vybihal, Joseph P (Winter)
3 hours
Corequisite: COMP 206.
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 2017, Winter 2018
Instructors: Ferreira Ruiz, Francisco; Pientka, Brigitte (Fall) Verbrugge, Clark (Winter)
3 hours
Prerequisite: COMP 250
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 2017, Winter 2018
Instructors: Robillard, Martin (Fall) Vybihal, Joseph P (Winter)
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 2017, Winter 2018
Instructors: Harmouche, Rola (Fall) Harmouche, Rola (Winter)
3 hours
Prerequisite: COMP 273
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 2017
Instructors: Chang, Xiao-Wen (Fall)
Computer Science (Sci) : Basic algorithmic techniques, their applications and limitations. Problem complexity, how to deal with problems for which no efficient solutions are known.
Terms: Winter 2018
Instructors: Cai, Yang (Winter)
Computer Science (Sci) : A research project in any area of computer science, involving a programming effort and/or a theoretical investigation, and supervised by a faculty member in the School of Computer Science. Final written report required.
Terms: Fall 2017, Winter 2018, Summer 2018
Instructors: Friedman, Nathan (Fall) Friedman, Nathan (Winter) Friedman, Nathan (Summer)
3 hours
Prerequisites: 15 Computer Science credits.
Restriction: For Honours students, or non-Honours students with permission of the department.
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 2017, Winter 2018, Summer 2018
Instructors: Drury, Stephen W; Laaksonen, Niko (Fall) Drury, Stephen W (Winter) Al Balushi, Ibrahim (Summer)
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 2017, Winter 2018
Instructors: Nica, Bogdan Lucian (Fall) Kelome, Djivede (Winter)
Mathematics & Statistics (Sci) : Mathematical foundations of logical thinking and reasoning. Mathematical language and proof techniques. Quantifiers. Induction. Elementary number theory. Modular arithmetic. Recurrence relations and asymptotics. Combinatorial enumeration. Functions and relations. Partially ordered sets and lattices. Introduction to graphs, digraphs and rooted trees.
Terms: Fall 2017, Winter 2018
Instructors: Decorte, Philip Evan (Fall) Seamone, Benjamin (Winter)
Mathematics & Statistics (Sci) : Review of mathematical writing, proof techniques, graph theory and counting. Mathematical logic. Graph connectivity, planar graphs and colouring. Probability and graphs. Introductory group theory, isomorphisms and automorphisms of graphs. Enumeration and listing.
Terms: Winter 2018
Instructors: Norin, Sergey (Winter)
Mathematics & Statistics (Sci) : Graph models. Graph connectivity, planarity and colouring. Extremal graph theory. Matroids. Enumerative combinatorics and listing.
Terms: Fall 2017
Instructors: Volec, Jan (Fall)
6 credits selected from:
Mathematics & Statistics (Sci) : Propositional calculus, truth-tables, switching circuits, natural deduction, first order predicate calculus, axiomatic theories, set theory.
Terms: Fall 2017
Instructors: Sabok, Marcin (Fall)
Fall
Restriction: Not open to students who are taking or have taken PHIL 210
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 2017, Winter 2018, Summer 2018
Instructors: Wolfson, David B (Fall) Su, Chien-Lin (Winter) Kelome, Djivede (Summer)
Mathematics & Statistics (Sci) : Sampling distributions, point and interval estimation, hypothesis testing, analysis of variance, contingency tables, nonparametric inference, regression, Bayesian inference.
Terms: Fall 2017, Winter 2018
Instructors: Khalili Mahmoudabadi, Abbas (Fall) Asgharian-Dastenaei, 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.
The remaining credits selected from computer science courses at the 300 level or above (except COMP 364 and COMP 396) and ECSE 539. At least 12 credits must be at the 500 level.