Program Requirements
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.
Required Courses (48 credits)
* Students who have sufficient knowledge in a programming language do not need to take COMP 202.
** Students take either MATH 340 or MATH 350.

COMP 202 Foundations of Programming (3 credits) *
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 2019, Winter 2020
Instructors: Elizabeth Patitsas, Giulia Alberini (Fall) Giulia Alberini (Winter)
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

COMP 206 Introduction to Software Systems (3 credits)
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 2019, Winter 2020
Instructors: Joseph P Vybihal (Fall) Joseph P Vybihal (Winter)

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), Data structures (arrays, stacks, queues, linked lists, trees, binary trees, binary search trees, heaps, hash tables), Recursive and nonrecursive algorithms (searching and sorting, tree and graph traversal). Abstract data types, inheritance. Selected topics.
Terms: Fall 2019, Winter 2020
Instructors: Michael Langer, Giulia Alberini (Fall) Giulia Alberini (Winter)

COMP 252 Honours Algorithms and Data Structures (3 credits)
Overview
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 2020
Instructors: Luc P Devroye (Winter)
3 hours
Restrictions: (1) Open only to students in Honours programs. (2) Students cannot receive credit for both COMP 251 and COMP 252.
COMP 252 uses basic combinatorial counting methods that are covered in MATH 240 but not in MATH 235. Students who are unfamiliar with these methods should speak with the instructor for guidance.

COMP 273 Introduction to Computer Systems (3 credits)
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 2019, Winter 2020
Instructors: Joseph P Vybihal (Fall) Kaleem Siddiqi (Winter)
3 hours
Corequisite: COMP 206.

COMP 302 Programming Languages and Paradigms (3 credits)
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 2019, Winter 2020
Instructors: Brigitte Pientka, Jacob Errington (Fall) Prakash Panangaden (Winter)
3 hours
Prerequisite: COMP 250

COMP 303 Software Design (3 credits)
Overview
Computer Science (Sci) : Principles, mechanisms, techniques, and tools for objectoriented software design and its implementation, including encapsulation, design patterns, and unit testing.
Terms: Fall 2019, Winter 2020
Instructors: Martin Robillard (Fall) Jin Guo (Winter)

COMP 310 Operating Systems (3 credits)
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 2019, Winter 2020
Instructors: Muthucumaru Maheswaran (Fall) Joseph P Vybihal (Winter)
3 hours
Prerequisite: COMP 273

COMP 330 Theory of Computation (3 credits)
Overview
Computer Science (Sci) : Finite automata, regular languages, contextfree languages, pushdown automata, models of computation, computability theory, undecidability, reduction techniques.
Terms: Fall 2019
Instructors: Claude Crepeau (Fall)
3 hours
Prerequisite: COMP 251.

COMP 350 Numerical Computing (3 credits)
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. Leastsquares approximation. Iterative methods for solving a nonlinear equation. Discretization methods for integration and differential equations.
Terms: Fall 2019
Instructors: Ivo Panayotov, Sitao Luan (Fall)

COMP 362 Honours Algorithm Design (3 credits)
Overview
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 2020
Instructors: Bruce Alan Reed (Winter)

COMP 400 Project in Computer Science (4 credits)
Overview
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 2019, Winter 2020
Instructors: Jorg Andreas Kienzle (Fall) Jorg Andreas Kienzle (Winter)
3 hours
Prerequisites: 15 Computer Science credits.
Restriction: For Honours students, or nonHonours students with permission of the department.

MATH 222 Calculus 3 (3 credits)
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 2019, Winter 2020
Instructors: Jeremy Macdonald, Broderick Causley (Fall) Jérôme Fortier (Winter)

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 2019, Winter 2020
Instructors: Djivede Kelome (Fall) Jeremy Macdonald (Winter)

MATH 240 Discrete Structures (3 credits)
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 2019, Winter 2020
Instructors: Jeremy Macdonald, Bogdan Nica (Fall) Jeremy Macdonald (Winter)

MATH 340 Discrete
Mathematics (3 credits) **
Overview
Mathematics & Statistics (Sci) : Discrete Mathematics and applications. Graph Theory: matchings, planarity, and colouring. Discrete probability. Combinatorics: enumeration, combinatorial techniques and proofs.
Terms: Winter 2020
Instructors: Jérôme Fortier (Winter)

MATH 350 Honours Discrete Mathematics
(3 credits) **
Overview
Mathematics & Statistics (Sci) : Discrete mathematics. Graph Theory: matching theory, connectivity, planarity, and colouring; graph minors and extremal graph theory. Combinatorics: combinatorial methods, enumerative and algebraic combinatorics, discrete probability.
Terms: Fall 2019
Instructors: Sergey Norin (Fall)
Complementary Courses (27 credits)
6 credits selected from:

MATH 318 Mathematical Logic (3 credits)
Overview
Mathematics & Statistics (Sci) : Propositional logic: truthtables, formal proof systems, completeness and compactness theorems, Boolean algebras; firstorder logic: formal proofs, Gödel's completeness theorem; axiomatic theories; set theory; Cantor's theorem, axiom of choice and Zorn's lemma, Peano arithmetic; Gödel's incompleteness theorem.
Terms: Fall 2019
Instructors: Marcin Sabok (Fall)

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 2019, Winter 2020
Instructors: Jose Andres Correa, Shomoita Alam (Fall) Djivede Kelome, David B Wolfson (Winter)

MATH 324 Statistics (3 credits)
Overview
Mathematics & Statistics (Sci) : Sampling distributions, point and interval estimation, hypothesis testing, analysis of variance, contingency tables, nonparametric inference, regression, Bayesian inference.
Terms: Fall 2019, Winter 2020
Instructors: Masoud AsgharianDastenaei (Fall)
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.