Program Requirements
Mentor: Professor A. Kelome, Department of Mathematics and Statistics, Faculty of Science
Program Prerequisites

MATH 133 Linear Algebra and Geometry (3 credits)
Overview
Mathematics & Statistics (Sci) : Systems of linear equations, matrices, inverses, determinants; geometric vectors in three dimensions, dot product, cross product, lines and planes; introduction to vector spaces, linear dependence and independence, bases. Linear transformations. Eigenvalues and diagonalization.
Terms: Fall 2020, Winter 2021
Instructors: BélangerRioux, Rosalie; Przytycki, Piotr; Ball, Gavin; Aigner, Florian (Fall) Kelome, Djivede (Winter)
3 hours lecture, 1 hour tutorial
Prerequisite: a course in functions
Restriction A: Not open to students who have taken MATH 221 or CEGEP objective 00UQ or equivalent.
Restriction B: Not open to students who have taken or are taking MATH 123, except by permission of the Department of Mathematics and Statistics.
Restriction C: Not open to students who are taking or have taken MATH 134.

MATH 140 Calculus 1 (3 credits)
Overview
Mathematics & Statistics (Sci) : Review of functions and graphs. Limits, continuity, derivative. Differentiation of elementary functions. Antidifferentiation. Applications.
Terms: Fall 2020, Winter 2021
Instructors: Trudeau, Sidney; Ghaswala, Tyrone; Albanese, Michael (Fall) Fortier, Jérôme (Winter)
3 hours lecture, 1 hour tutorial
Prerequisite: High School Calculus
Restriction: Not open to students who have taken MATH 120, MATH 139 or CEGEP objective 00UN or equivalent
Restriction: Not open to students who have taken or are taking MATH 122, except by permission of the Department of Mathematics and Statistics
Each Tutorial section is enrolment limited

MATH 141 Calculus 2 (4 credits)
Overview
Mathematics & Statistics (Sci) : The definite integral. Techniques of integration. Applications. Introduction to sequences and series.
Terms: Fall 2020, Winter 2021
Instructors: Fortier, Jérôme; Sabok, Marcin (Fall) Trudeau, Sidney (Winter)
or their equivalents
Required Courses (12 credits)

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 2020, Winter 2021
Instructors: Fortier, Jérôme; Kelome, Djivede (Fall) Vetois, Jerome (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 2020, Winter 2021
Instructors: Pichot, Michael (Fall)

MATH 315 Ordinary Differential Equations (3 credits)
Overview
Mathematics & Statistics (Sci) : First order ordinary differential equations including elementary numerical methods. Linear differential equations. Laplace transforms. Series solutions.
Terms: Fall 2020, Winter 2021
Instructors: Paquette, Courtney (Fall)

MGSC 373 Operations Research 1 (3 credits)
Overview
Management Science : A realistic experience of analytical models which have been successfully applied in several areas of managerial decisionmaking like marketing, finance and IS. Emphasis on the formulation of problems, their solution approaches, limitations, underlying assumptions and practical use. Topics include: decision analysis, project management, simulation, linear and integer programming, sensitivity analysis.
Terms: Fall 2020
Instructors: Smith, Brian E (Fall)
Prerequisite: MGCR 271
Complementary Courses (6 credits)
Maximum of 3 credits from:

MGSC 372 Advanced Business Statistics (3 credits)
Overview
Management Science : A practical managerial approach to advanced simple and multiple regression analysis, with application in finance, economics and business, including a review of probability theory, an introduction to methods of least squares and maximum likelihood estimation, autoregressive forecasting models and analysis of variance.
Terms: Fall 2020, Winter 2021
Instructors: Smith, Brian E (Fall) Smith, Brian E (Winter)
Prerequisite: MGCR 271

MGSC 479 Applied Optimization (3 credits)
Overview
Management Science : Applications of optimization models to management problems, including Linear Programming, Integer Programming and Nonlinear Programming.
Terms: This course is not scheduled for the 20202021 academic year.
Instructors: There are no professors associated with this course for the 20202021 academic year.
Prerequisite: MGSC 373.

MGSC 575 Applied Time Series Analysis Managerial Forecasting (3 credits)
Overview
Management Science : Management applications of time series analysis. Starting with ratiotomoving average methods, the course deals successively with Census 2, exponential smoothing methods, the methodology introduced by Box and Jenkins, spectral analysis and timeseries regression techniques. Computational aspects and applications of the methodology are emphasized.
Terms: This course is not scheduled for the 20202021 academic year.
Instructors: There are no professors associated with this course for the 20202021 academic year.
Prerequisite (Undergraduate): MGCR 271.
Restriction: Open to U2 and U3 students.

MGSC 578 Simulation of Management Systems (3 credits)
Overview
Management Science : Building simulation models of management systems. Design of simulation experiments and the analysis and implementation of results. Students are expected to design a complete simulation of a real problem using a standard simulation language.
Terms: Winter 2021
Instructors: There are no professors associated with this course for the 20202021 academic year.
Prerequisite: (Undergraduate) MGCR 271.
Restriction: Open to U2 and U3 students.
The remaining 3 credits selected from:

MATH 316 Complex Variables (3 credits)
Overview
Mathematics & Statistics (Sci) : Algebra of complex numbers, CauchyRiemann equations, complex integral, Cauchy's theorems. Taylor and Laurent series, residue theory and applications.
Terms: Fall 2020
Instructors: Pym, Brent (Fall)

MATH 317 Numerical Analysis (3 credits)
Overview
Mathematics & Statistics (Sci) : Error analysis. Numerical solutions of equations by iteration. Interpolation. Numerical differentiation and integration. Introduction to numerical solutions of differential equations.
Terms: Fall 2020
Instructors: Bartello, Peter (Fall)

MATH 319 Introduction to Partial Differential Equations (3 credits)
Overview
Mathematics & Statistics (Sci) : First order equations, geometric theory; second order equations, classification; Laplace, wave and heat equations, SturmLiouville theory, Fourier series, boundary and initial value problems.
Terms: Winter 2021
Instructors: Bartello, Peter (Winter)

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 2020, Winter 2021
Instructors: Sajjad, Alia; Wolfson, David B (Fall) Wolfson, David B; Sajjad, Alia (Winter)

MATH 326 Nonlinear Dynamics and Chaos (3 credits)
Overview
Mathematics & Statistics (Sci) : Linear systems of differential equations, linear stability theory. Nonlinear systems: existence and uniqueness, numerical methods, one and two dimensional flows, phase space, limit cycles, PoincareBendixson theorem, bifurcations, Hopf bifurcation, the Lorenz equations and chaos.
Terms: Fall 2020
Instructors: Nave, JeanChristophe (Fall)

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 2021
Instructors: Fortier, Jérôme (Winter)

MATH 407 Dynamic Programming (3 credits)
Overview
Mathematics & Statistics (Sci) : Sequential decision problems, resource allocation, transportation problems, equipment replacement, integer programming, network analysis, inventory systems, project scheduling, queuing theory calculus of variations, markovian decision processes, stochastic path problems, reliability, discrete and continuous control processes.
Terms: This course is not scheduled for the 20202021 academic year.
Instructors: There are no professors associated with this course for the 20202021 academic year.

MATH 417 Linear Optimization (3 credits)
Overview
Mathematics & Statistics (Sci) : An introduction to linear optimization and its applications: Duality theory, fundamental theorem, sensitivity analysis, convexity, simplex algorithm, interiorpoint methods, quadratic optimization, applications in game theory.
Terms: Fall 2020
Instructors: Hoheisel, Tim (Fall)