Program Requirements
This program is the standard Major program offered by the School of Computer Science. It provides a broad introduction to the principles of computer science and offers ample opportunity to acquire indepth knowledge of several subdisciplines. At the same time, its credit requirements allow students to take an additional minor.
Students may complete this program with a minimum of 60 credits or a maximum of 63 credits depending if they are exempt from taking COMP 202.
Required Courses (33 credits)
* Students who have sufficient knowledge in a programming language do not need to take COMP 202.

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 2016, Winter 2017, Summer 2017
Instructors: Kaleem Siddiqi, Melanie LymanAbramovitch, Daniel Pomerantz (Fall) Bentley Oakes, 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 2016, Winter 2017
Instructors: David Meger (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 2016, Winter 2017
Instructors: Michael Langer (Fall) Mathieu Blanchette (Winter)

COMP 251 Algorithms and Data Structures (3 credits)
Overview
Computer Science (Sci) : Introduction to algorithm design and analysis. Graph algorithms, greedy algorithms, data structures, dynamic programming, maximum flows.
Terms: Fall 2016, Winter 2017
Instructors: Claude Crepeau (Fall) Jérôme Waldispuhl (Winter)

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 2016, Winter 2017
Instructors: Paul Kry (Fall) Joseph P Vybihal (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 2016, Winter 2017
Instructors: Clark Verbrugge (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 2016, Winter 2017
Instructors: Joseph P Vybihal (Fall) Martin Robillard (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 2016, Winter 2017
Instructors: Muthucumaru Maheswaran (Fall) Muthucumaru Maheswaran (Winter)
3 hours
Prerequisite: COMP 273

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 2016, Winter 2017, Summer 2017
Instructors: Stephen W Drury, Thomas F Fox (Fall) Alexander Garver (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 2016, Winter 2017
Instructors: Bogdan Lucian Nica (Fall) Michael Pichot (Winter)

MATH 240 Discrete Structures 1 (3 credits)
Overview
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 2016, Winter 2017
Instructors: Frederick Shepherd (Winter)
Complementary Courses (30 credits)
Students should talk to an academic adviser before choosing their complementary courses.
At least 6 credits selected from:
 COMP 330 Theory of Computation (3 credits)

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 2016
Instructors: XiaoWen Chang (Fall)

COMP 360 Algorithm Design (3 credits)
Overview
Computer Science (Sci) : Advanced algorithm design and analysis. Linear programming, complexity and NPcompleteness, advanced algorithmic techniques.
Terms: Fall 2016, Winter 2017
Instructors: Yang Cai (Fall) Yang Cai (Winter)
39 credits selected from:
* Must include at least one of MATH 323 and MATH 340.

MATH 318 Mathematical Logic (3 credits)
Overview
Mathematics & Statistics (Sci) : Propositional calculus, truthtables, switching circuits, natural deduction, first order predicate calculus, axiomatic theories, set theory.
Terms: Fall 2016
Instructors: Marcin Sabok (Fall)
Fall
Restriction: Not open to students who are taking or have taken PHIL 210

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 2016, Winter 2017, Summer 2017
Instructors: Masoud AsgharianDastenaei (Fall) Sanchayan Sen (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 2016, Winter 2017
Instructors: MariePier Côté (Fall) Masoud AsgharianDastenaei (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.

MATH 340 Discrete Structures 2 (3 credits) *
Overview
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 2017
Instructors: Sergey Norin (Winter)
At least 6 credits at the 400level or above.
The remaining credits selected from computer science courses at the 300 level or above (except COMP 364 and COMP 396) and ECSE 539.
Note: Students have to make sure that they have the appropriate prerequisites when choosing upperlevel courses.