Vector spaces, direct sums, linear transformations, eigenvalues, eigenvectors, inner product spaces, self-adjoint operators, positive operators, singular-value decomposition, minimal polynomials, Jordan canonical form, the projection theorem, applications to approximation and optimization problems.
(Lec: 3, Lab: 0, Tut: 0.5)

Introduction to algebraic systems and structures, and their engineering applications. Topics include symbolic logic; switching and logic circuits; set theory, equivalence relations and mappings; the integers and modular arithmetic; groups, cyclic groups, Lagrange's theorem, group quotients, group homomorphisms and isomorphisms; applications to error-control codes for noisy communication channels.
(Lec: 3, Lab: 0, Tut: 0.5)

The course will discuss the application of linear differential equations with constant coefficients, and systems of linear equations within the realm of civil engineering. Additionally, the course will explore relevant data analysis techniques including: graphical and statistical analysis and presentation of experimental data, random sampling, estimation using confidence intervals, linear regression, residuals and correlation.
(Lec: 3, Lab: 0.4, Tut: 0.8)

First order differential equations, linear differential equations with constant coefficients, and applications, Laplace transforms, systems of linear equations.
(Lec: 3, Lab: 0, Tut: 0.5)

Review of multiple integrals. Differentiation and integration of vectors; line, surface and volume integrals; gradient, divergence and curl; conservative fields and potential. Spherical and cylindrical coordinates, solid angle. Green's and Stokes' theorems, the divergence theorem.
(Lec: 3, Lab: 0, Tut: 0)

Complex arithmetic, complex plane. Differentiation, analytic functions. Elementary functions. Elementary functions. Contour integration, Cauchy's Theorem and Integral Formula. Taylor and Laurent series, residues with applications to evaluation of integrals.
(Lec: 3, Lab: 0, Tut: 0.5)

Topics include developing and analyzing mathematical models describing physical and natural phenomena and those arising in electrical engineering applications (such as circuits), classification of differential equations, methods for solving differential equations, Laplace Transform method, systems of differential equations and connections with Linear Algebra, stability of systems.
(Lec: 3, Lab: 0, Tut: 0.5)

Topics include developing and analyzing mathematical models describing physical and natural dynamical phenomena and those arising in various engineering system applications, classification of differential equations, methods for solving differential equations, Laplace Transform method, systems of differential equations and connections with Linear Algebra, stability of linear and nonlinear systems and Lyapunov's method.
(Lec: 3, Lab: 0, Tut: 0.5)

An introductory course on the effective use of computers in science and engineering. Topics include: solving linear and nonlinear equations, interpolation, integration, and numerical solution of ordinary differential equations. Extensive use is made of MATLAB, a high level interactive numerical package.
NOT OFFERED 2022-2023
(Lec: 3, Lab: 0.5, Tut: 0)

Limits, Continuity, C', and linear approximations of functions of several variables. Multiple integrals and Jacobians, Line and surface integrals. The theorems of Green, Stokes, and Gauss.
(Lec: 3, Lab: 0, Tut: 0.5)

Taylor's theorem, optimization, implicit and inverse function theorems. Elementary topology of Euclidean spaces. Sequences and series of numbers and functions. Pointwise and uniform convergence. Power series.
(Lec: 3, Lab: 0, Tut: 0.5)

Complex numbers, analytic functions, harmonic functions. Cauchy's theorem. Taylor and Laurent series. Calculus of residues. Rouche's theorem.
(Lec: 3, Lab: 0, Tut: 0.5)

Topological notions on Euclidean spaces, continuity and differentiability of functions of several variables, uniform continuity, extreme value theorem, implicit function theorem, completeness and Banach spaces, Picard-Lindelöf theorem, applications to constrained optimization and Lagrange multipliers, and existence/uniqueness of solutions to systems of differential equations.
(Lec: 3, Lab: 0, Tut: 0)

Modeling control systems, linearization around an equilibrium point.
Block diagrams, impulse response, transfer function, frequency response. Controllability and observability, LTI realizations. Feedback and stability,
Lyapunov stability criterion, pole placement, Routh criterion. Input/output
stability, design of PID controllers, Bode plots, Nyquist plots, Nyquist stability criterion, robust controllers. Laboratory experiments illustrate the control
concepts learned in class.
NOT OFFERED 2023-2024
(Lec: 3, Lab: 0.5, Tut: 0.5)

Banach and Hilbert spaces of continuous- and discrete-time signals; spaces of continuous and not necessarily continuous signals; continuous-discrete Fourier transform; continuous-continuous Fourier transform; discrete-continuous Fourier transform; discrete-discrete Fourier transform; transform inversion using Fourier series and Fourier integrals.
NOT OFFERED 2023-2024
(Lec: 3, Lab: 0, Tut: 0.5)

Review of signal spaces arising in systems theory and applications, such as linear spaces, Banach and Hilbert spaces, and distributions. Approximation and representation of signals. Discrete and continuous Fourier Transforms, Laplace and Z transforms. Linear input/output systems and their stability and regularity analysis. Frequency-domain and time-domain analysis of linear time-invariant systems. Applications to modulation of communication signals, linear filter design, system design, control design, and digital sampling.
(Lec: 3, Lab: 0, Tut: 0.5)

Topics include Markov chains, Introduction to dynamic programming and Markov Decision Processes, simulation, queuing theory, inventory theory, and introduction to reinforcement learning.
(Lec: 3, Lab: 0, Tut: 0)

Methods and theory for ordinary and partial differential equations; separation
of variables in rectangular and cylindrical coordinate systems; sinusoidal and Bessel orthogonal functions; the wave, diffusion, and Laplace's equation;
Sturm-Liouville theory; Fourier transform techniques.
NOT OFERED 2023-2024
(Lec: 3, Lab: 0, Tut: 0.5)

This course highlights the usefulness of game theoretical approaches in solving problems in the natural sciences and economics. Basic ideas of game theory, including Nash equilibrium and mixed strategies; stability using approaches developed for the study of dynamical systems, including evolutionary
stability and replicator dynamics; the emergence of co-operative behaviour; limitations of applying the theory to human behaviour.
(Lec: 3, Lab: 0, Tut: 0)

Introduction to probability theory and its applications in engineering science: basic concepts of probability, counting, conditional probability, Bayes' rule, independence; probability models; random variables, distribution functions, probability mass functions, probability density functions; expectation, variance, moments; jointly distributed random variables; transformations of random variables. Distributions: Bernoulli, binomial, geometric, negative binomial, Poisson, uniform, exponential, normal. Applications: elementary stochastic processes, noisy communication channels.
(Lec: 3, Lab: 0, Tut: 0.5)

Intermediate probability theory as a basis for further study in mathematical statistics and stochastic processes and applications; probability measures, expectations; modes of convergence of sequences of random variables; conditional expectations; independent systems of random variables; Gaussian systems; characteristic functions; Law of large numbers, Central limit theory; some notions of dependence.
(Lec: 3, Lab: 0, Tut: 0)

Exploratory data analysis -- graphical and statistical analysis and presentation
of experimental data. Random sampling. Probability and probability models
for discrete and continuous random variables. Process capability. Normal probability graphs. Sampling distribution of means and proportions. Statistical Quality Control and Statistical Process Control. Estimation using confidence intervals. Testing of hypothesis procedures for means, variances and
proportions -- one and two samples cases. Liner regression, residuals and correlation. ANOVA. Use of statistical software.
NOT OFFERED 2023-2024
(Lec: 3, Lab: 0, Tut: 0.5)

This is a project-based design course where methods of applied mathematics are used to solve a complex open-ended engineering problem. The projects involve using system theoretic methods for modeling, analysis, and design applied to engineering problems arising in a variety of engineering disciplines. Students will work in teams and employ design processes to arrive at a solution. The course will include elements of communications, economic analysis, impacts of engineering, professionalism, and engineering ethics.
K4(Lec: Yes, Lab: Yes, Tut: Yes)

Construction and properties of finite fields. Polynomials, vector spaces,
block codes over finite fields. Hamming distance and other code parameters. Bounds relating code parameters. Cyclic codes and their structure as ideals. Weight distribution. Special codes and their relation to designs and projective planes. Decoding algorithms.
NOT OFERED 2023-2024.
(Lec: 3, Lab: 0, Tut: 0)

Time estimates for arithmetic and elementary number theory algorithms
(division algorithm, Euclidean algorithm, congruences), modular arithmetic,
finite fields, quadratic residues. Simple cryptographic systems; public key,
RSA. Primality and factoring: pseudoprimes, Pollard's rho-method, index
calculus. Elliptic curve cryptography.
(Lec: 3, Lab: 0, Tut: 0)

This course covers core topics in both classical and modern control theory. Overview of classical control theory using frequency methods. Linear and nonlinear controlled differential systems and their solutions. Stabilization and stability methods via Lyapunov analysis or linearization. Controllability, observability, minimal realizations, feedback stabilization, observer design. Optimal control theory, the linear quadratic regulator, dynamic programming.
(Lec: 3, Lab: 0.5, Tut: 0.5)

Continuum mechanics lays the foundations for the study of the mechanical behavior of solids and fluids. After a review of vector and tensor analysis, the kinematics of continua are introduced. Emphasis is given to the concepts of stress, strain and deformation. The fundamental laws of conservation of mass, balances of (linear and angular) momentum and energy are presented together with the constitutive models. Applications of these models are given in the theory of linearized elasticity and fluid dynamics.
(Lec: 3, Lab: 0, Tut: 0.5)

Theory of convex sets and functions; separation theorems; primal-dual properties; geometric treatment of optimization problems; algorithmic procedures for solving constrained optimization programs; applications of optimization theory to machine learning.
NOT OFFERED 2023-2024
(Lec: 3, Lab: 0, Tut: 0.5)

Topic: An Introduction to Stochastic Differential Equations (with Applications to Mathematical Finance and Engineering)
The aim of this course is to provide a rigorous introduction to the theory of stochastic calculus and stochastic differential equations, and to survey some of its most important applications, especially in Mathematical Finance. The Ito stochastic integral and its associated Ito Calculus will be derived in the general framework of continuous semimartingales, leading to a detailed treatment of stochastic differential equations (SDEs) and their properties. The theory thus developed will be applied to selected problems in Mathematical Finance (option pricing and hedging, trading strategies and arbitrage) and Engineering (boundary-value problems, filtering, optimal control). Numerical aspects of SDEs will also be discussed.
(Lec: 3, Lab: 0, Tut: 0.5)

Geometric modelling, including configuration space, tangent bundle, kinetic
energy, inertia, and force. Euler-Lagrange equations using affine connections.
The last part of the course develops one of the following three applications: mechanical systems with nonholonomic constraints; control theory for
mechanical systems; equilibria and stability.
NOT OFFERED 2023-2024
(Lec: 3, Lab: 0, Tut: 0.5)

Many systems evolve with an inherent amount of randomness in time and/or space. The focus of this course is on developing and analyzing methods for analyzing time series. Because most of the common time--domain methods
are unreliable, the emphasis is on frequency--domain methods, i.e. methods
that work and expose the bias that plagues most time--domain techniques.
Slepian sequences (discrete prolate spheroidal sequences) and multi--taper methods of spectrum estimation are covered in detail.
NOT OFFERED 2023-2024
(Lec: 3, Lab: 0, Tut: 0)

Markov chains, birth and death processes, random walk problems, elementary renewal theory, Markov processes, Brownian motion and Poisson processes, queuing theory, branching processes.
(Lec: 3, Lab: 0, Tut: 0.5)

Introduction to the theory and application of statistical algorithms. Topics include classification, smoothing, model selection, optimization, sampling, supervised and unsupervised learning.
(Lec: 3, Lab: 0, Tut: 0)

This course concerns the optimization, control, and stabilization of dynamical systems under probabilistic uncertainty with applications in engineering systems and applied mathematics. Topics include: controlled and control-free Markov chains and stochastic stability; martingale methods for stability and stochastic learning; dynamic programming and optimal control for finite horizons, infinite horizons, and average cost problems; partially observed models, non-linear filtering and Kalman Filtering; linear programming and numerical methods; reinforcement learning and stochastic approximation methods; decentralized stochastic control, and continuous-time stochastic control.
(Lec: 3, Lab: 0, Tut: 0.5)

Topics include: information measures, entropy, mutual information, modeling of information sources, lossless data compression, block encoding, variable-length encoding, Kraft inequality, fundamentals of channel coding, channel capacity, rate-distortion theory, lossy data compression, rate-distortion theorem.
(Lec: 3, Lab: 0, Tut: 0.5)

Topics include: arithmetic coding, universal lossless coding, Lempel-Ziv and related dictionary based methods, rate-distortion theory, scalar and vector quantization, predictive and transform coding, applications to speech and image coding.
(Lec: 3, Lab: 0, Tut: 0)

Subject matter will vary from year to year. Possible subjects include: constrained coding and applications to magnetic and optical recording; data compression; theory and practice of error-control coding; design and performance analysis
of communication networks; and other related topics.
NOT OFFERED 2023-2024
(Lec: 3, Lab: 0, Tut: 0)

This course covers performance models for data networking, delay models
and loss models; analysis of multiple access systems, routing, and flow control; multiplexing; priority systems; satellite multiple access, wireless networking, wireless sensor networks. Knowledge of networking protocols is not required.
NOT OFFERED 2023-2024
(Lec: 3, Lab: 0, Tut: 0)

This is the capstone design course for Mathematics and Engineering. Students must work in groups. Projects are selected early in the year from a list put forward by Mathematics and Engineering faculty members who will also supervise the projects. There is a heavy emphasis on engineering design and professional practice. All projects must be open-ended and design oriented, and students are expected to undertake and demonstrate, in presentations and written work, a process by which the design facets of the project are approached. Projects must involve social, environmental, and economic factors, and students are expected to address these factors comprehensively in presentations and written work. Students are assessed individually and as a group on their professional conduct during the course of the project.
K7.5(Lec: No, Lab: Yes, Tut: Yes)

This is a seminar and course, with an emphasis on communication skills and professional practice. A writing module develops technical writing skills. Students give an engineering presentation to develop their presentation skills. Seminars are given by faculty from the Mathematics and Engineering program, by Mathematics and Engineering alumni on the career paths since completing the program, and by visiting speakers on a variety of professional practice matters, on topics such as workplace safety, workplace equity and human rights, and professional organizations. Open to Mathematics and Engineering students only.
(Lec: 3, Lab: 0, Tut: 0)