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An elementary introduction to computing science and computer programming, suitable for students with little or no programming background. Students will learn fundamental concepts and terminology of computing science, acquire elementary skills for programming in a high-level language, e.g. Python. The students will be exposed to diverse fields within, and applications of computing science. Topics will include: pseudocode; data types and control structures; fundamental algorithms; recursion; reading and writing files; measuring performance of algorithms; debugging tools; basic terminal navigation using shell commands. Treatment is informal and programming is presented as a problem-solving tool. Prerequisite: BC Math 12 or equivalent is recommended. Students with credit for CMPT 102, 128, 130 or 166 may not take this course for further credit. Students who have taken CMPT 125, 129, 130 or 135 first may not then take this course for further credit. Quantitative/Breadth-Science.

A rigorous introduction to computing science and computer programming, suitable for students who already have some background in computing science and programming. Intended for students who will major in computing science or a related program. Topics include: memory management; fundamental algorithms; formally analyzing the running time of algorithms; abstract data types and elementary data structures; object-oriented programming and software design; specification and program correctness; reading and writing files; debugging tools; shell commands. Prerequisite: CMPT 120 or CMPT 130, with a minimum grade of C-. Students with credit for CMPT 126, 129, 135 or CMPT 200 or higher may not take this course for further credit. Quantitative.

An introduction to computing science and computer programming, using a systems oriented language, such as C or C++. This course introduces basic computing science concepts. Topics will include: elementary data types, control structures, functions, arrays and strings, fundamental algorithms, computer organization and memory management. Prerequisite: BC Math 12 (or equivalent, or any of MATH 100, 150, 151, 154, or 157, with a minimum grade of C-). Students with credit for CMPT 102, 120, 128 or 166 may not take this course for further credit. Students who have taken CMPT 125, 129 or 135 first may not then take this course for further credit. Quantitative/Breadth-Science.

A second course in systems-oriented programming and computing science that builds upon the foundation set in CMPT 130 using a systems-oriented language such as C or C++. Topics: a review of the basic elements of programming; introduction to object-oriented programming (OOP); techniques for designing and testing programs; use and implementation of elementary data structures and algorithms; introduction to embedded systems programming. Prerequisite: CMPT 130 with a minimum grade of C-. Students with credit for CMPT 125, 126, or 129 may not take this course for further credit. Quantitative.

Using a mathematical software package for doing calculations in linear algebra. Development of computer models that analyze and illustrate applications of linear algebra. All calculations and experiments will be done in the Matlab software package. Topics include: large-scale matrix calculations, experiments with cellular automata, indexing, searching and ranking pages on the internet, population models, data fitting and optimization, image analysis, and cryptography. Prerequisite: One of CMPT 102, 120, 126, 128 or 130 and one of MATH 150, 151, 154 or 157 and one of MATH 232 or 240. MATH 232 or 240 can be taken as corequisite. Quantitative.

Using a mathematical software package for doing computations from calculus. Development of computer models that analyze and illustrate applications of calculus. All calculations and experiments will be done in the Maple software package. Topics include: graphing functions and data, preparing visual aids for illustrating mathematical concepts, integration, Taylor series, numerical approximation methods, 3D visualization of curves and surfaces, multi-dimensional optimization, differential equations and disease spread models. Prerequisite: One of CMPT 102, 120, 126, 128 or 130 and one of MATH 152, 155, or 158. Quantitative.

Mathematical induction. Limits of real sequences and real functions. Continuity and its consequences. The mean value theorem. The fundamental theorem of calculus. Series. Prerequisite: MATH 152 with a minimum grade of C-; or MATH 155 or 158 with a grade of B. Quantitative.

Rectangular, cylindrical and spherical coordinates. Vectors, lines, planes, cylinders, quadric surfaces. Vector functions, curves, motion in space. Differential and integral calculus of several variables. Vector fields, line integrals, fundamental theorem for line integrals, Green's theorem. Prerequisite: MATH 152 with a minimum grade of C-; or MATH 158 with a grade of at least B. Also, for students in the life sciences, MATH 154 with a minimum grade of C- and MATH 155 with a minimum grade of A-. Recommended: It is recommended that MATH 240 or 232 be taken before or concurrently with MATH 251. Quantitative.

Vector calculus, divergence, gradient and curl; line, surface and volume integrals; conservative fields, theorems of Gauss, Green and Stokes; general curvilinear coordinates and tensor notation. Introduction to orthogonality of functions, orthogonal polynomials and Fourier series. Prerequisite: MATH 240 or 232, and 251, all with a minimum grade of C-. MATH 240 or 232 may be taken concurrently. Students with credit for MATH 254 may not take MATH 252 for further credit. Quantitative.

First-order differential equations, second- and higher-order linear equations, series solutions, introduction to Laplace transform, systems and numerical methods, applications in the physical, biological and social sciences. Prerequisite: MATH 152 with a minimum grade of C-; or MATH 155 or 158, with a grade of at least B; MATH 232 or 240, with a minimum grade of C-. Students with credit for MATH 310 may not take this course for further credit. Quantitative.

Basic laws of probability, sample distributions. Introduction to statistical inference and applications. Prerequisite: or Corequisite: MATH 152 or 155 or 158, with a minimum grade of C-. Students wishing an intuitive appreciation of a broad range of statistical strategies may wish to take STAT 100 first. Quantitative.

Designed for students specializing in mathematics, physics, chemistry, computing science and engineering. Topics as for Math 151 with a more extensive review of functions, their properties and their graphs. Recommended for students with no previous knowledge of Calculus. In addition to regularly scheduled lectures, students enrolled in this course are encouraged to come for assistance to the Calculus Workshop (Burnaby), or Math Open Lab (Surrey). Prerequisite: Pre-Calculus 12 (or equivalent) with a grade of at least B+, or MATH 100 with a grade of at least B-. Students with credit for either MATH 151, 154 or 157 may not take MATH 150 for further credit. Quantitative.

Designed for students specializing in mathematics, physics, chemistry, computing science and engineering. Logarithmic and exponential functions, trigonometric functions, inverse functions. Limits, continuity, and derivatives. Techniques of differentiation, including logarithmic and implicit differentiation. The Mean Value Theorem. Applications of differentiation including extrema, curve sketching, Newton's method. Introduction to modeling with differential equations. Polar coordinates, parametric curves. Prerequisite: Pre-Calculus 12 (or equivalent) with a grade of at least A, or MATH 100 with a grade of at least B. Students with credit for either MATH 150, 154 or 157 may not take MATH 151 for further credit. Quantitative.

Designed for students specializing in the life sciences. Topics include: limits, growth rate and the derivative; elementary functions, optimization and approximation methods, and their applications, integration, and differential equations; mathematical models of biological processes and their implementation and analysis using software. Prerequisite: Pre-Calculus 12 (or equivalent) with a grade of at least B, or MATH 100 with a grade of at least C-, or MATH 110 with a grade of at least C-. Students with credit for either MATH 150, 151 or 157 may not take MATH 154 for further credit. Quantitative.

Designed for students specializing in business or the social sciences. Topics include: limits, growth rate and the derivative; logarithmic, exponential and trigonometric functions and their application to business, economics, optimization and approximation methods; introduction to functions of several variables with emphasis on partial derivatives and extrema. Prerequisite: Pre-Calculus 12 (or equivalent) with a grade of at least B, or MATH 100 with a grade of at least C, or MATH 110 with a grade of at least C. Students with credit for either MATH 150, 151 or 154 may not take MATH 157 for further credit. Quantitative.

Riemann sum, Fundamental Theorem of Calculus, definite, indefinite and improper integrals, approximate integration, integration techniques, applications of integration. First-order separable differential equations and growth models. Sequences and series, series tests, power series, convergence and applications of power series. Prerequisite: MATH 150 or 151 or 155, with a minimum grade of C-; or MATH 154 or 157, with a grade of at least B. Students with credit for MATH 158 or 251 may not take this course for further credit. Quantitative.

Designed for students specializing in the life sciences. Topics include: vectors and matrices, partial derivatives, multi-dimensional integrals, systems of differential equations, compartment models, graphs and networks, and their applications to the life sciences; mathematical models of multi-component biological processes and their implementation and analysis using software. Students planning to take MATH 251 are recommended to consider MATH 152 as the prerequisite pathway. See an advisor to determine what may be best for you. Prerequisite: MATH 150, 151 or 154, with a minimum grade of C-; or MATH 157 with a grade of at least B. Students with credit for MATH 152 or 158 may not take this course for further credit. Quantitative.

Designed for students specializing in business or the social sciences. Topics include: theory of integration, integration techniques, applications of integration; functions of several variables with emphasis on double and triple integrals and their applications; introduction to differential equations with emphasis on some special first-order equations and their applications; sequences and series. Prerequisite: MATH 150 or 151 or 154 or 157, with a minimum grade of C-. Students with credit for MATH 152 or 155 may not take MATH 158 for further credit. Quantitative.

Linear equations, matrices, determinants. Introduction to vector spaces and linear transformations and bases. Complex numbers. Eigenvalues and eigenvectors; diagonalization. Inner products and orthogonality; least squares problems. An emphasis on applications involving matrix and vector calculations. Prerequisite: MATH 150 or 151 or MACM 101, with a minimum grade of C-; or MATH 154 or 157, both with a grade of at least B. Students with credit for MATH 240 may not take this course for further credit. Quantitative.

Linear equations, matrices, determinants. Real and abstract vector spaces, subspaces and linear transformations; basis and change of basis. Complex numbers. Eigenvalues and eigenvectors; diagonalization. Inner products and orthogonality; least squares problems. Applications. Subject is presented with an abstract emphasis and includes proofs of the basic theorems. Prerequisite: MATH 150 or 151 or MACM 101, with a minimum grade of C-; or MATH 154 or 157, both with a grade of at least B. Students with credit for MATH 232 cannot take this course for further credit. Quantitative.

Introduction to a variety of practical and important data structures and methods for implementation and for experimental and analytical evaluation. Topics include: stacks, queues and lists; search trees; hash tables and algorithms; efficient sorting; object-oriented programming; time and space efficiency analysis; and experimental evaluation. Prerequisite: (MACM 101 and (CMPT 125, CMPT 129 or CMPT 135)) or (ENSC 251 and ENSC 252), all with a minimum grade of C-. Quantitative.

Fundamental electrical circuit quantities, and circuit elements; circuits laws such as Ohm law, Kirchoff's voltage and current laws, along with series and parallel circuits; operational amplifiers; network theorems; nodal and mesh methods; analysis of natural and step response of first (RC and RL), as well as second order (RLC) circuits; real, reactive and rms power concepts. In addition, the course will discuss the worker safety implications of both electricity and common laboratory practices such as soldering. Prerequisite: (PHYS 121 or PHYS 126 or PHYS 141), ENSC 120, MATH 232 and (MATH 260 or MATH 310), all with a minimum grade of C-. MATH 260 may be taken concurrently. Students with credit for MSE 250 or SEE 230 cannot take this course for further credit. Quantitative.

A continuation of MACM 101. Topics covered include graph theory, trees, inclusion-exclusion, generating functions, recurrence relations, and optimization and matching. Prerequisite: MACM 101 or (ENSC 251 and one of MATH 232 or MATH 240). Quantitative.

This course will cover the following topics: fundamental electrical circuit quantities, and circuit elements; circuits laws such as Ohm law, Kirchoff's voltage and current laws, along with series and parallel circuits; operational amplifiers; network theorems; nodal and mesh methods; analysis of natural and step response of first (RC and RL), as well as second order (RLC) circuits; real, reactive and rms power concepts. In addition, the course will discuss the worker safety implications of both electricity and common laboratory practices such as soldering. Prerequisite: PHYS 141 or (PHYS 121 and 131), MSE 281 (or MATH 260), and MATH 232. MSE 281 (or MATH 260) may be taken concurrently. Students with credit for SEE 230 or ENSC 220 may not take MSE 250 for further credit. Quantitative.

An intermediate mechanics course covering kinematics, dynamics, calculus of variations and Lagrange's equations, non-inertial reference frames, central forces and orbits, and rigid body motion. Prerequisite: MATH 251; MATH 232 or MATH 240; PHYS 255 or ENSC 380. All prerequisite courses require a minimum grade of C-. Recommended Corequisite: MATH 260 or MATH 310. Quantitative.

The concepts of quantum mechanics introduced through two-level systems and explored in a way that requires only familiarity with general concepts of linear algebra. Introduction to concepts in classical and quantum information theory, bits and qubits, quantum dynamics, quantum communication and cryptography, and quantum circuits. Prerequisite: Either MATH 232 or MATH 240, with a minimum grade of C-. Quantitative.

This course is a continuation of STAT 270. Review of probability models. Procedures for statistical inference using survey results and experimental data. Statistical model building. Elementary design of experiments. Regression methods. Introduction to categorical data analysis. Prerequisite: STAT 270 and one of MATH 152, MATH 155, or MATH 158, all with a minimum grade of C-. Quantitative.

Principles and concepts of transmission of genetic information. Topics include: molecular basis of inheritance, interpretation of pedigrees, chromosomal mapping, linkage, crosses, epigenetics, and the regulation and flow of genetic information from DNA to proteins. Focus on problem-solving. Prerequisite: BISC 101 and 102, both with a minimum grade of C-.

Introduces the different approaches used to study the natural world and explores ecological concepts and theory relating to animal behaviour, population dynamics, the distribution of species, structure of communities and the function of ecosystems. Prerequisite: BISC 101 and 102, both with a minimum grade of C-.

Principles and applications in health sciences research methodology. Quantitative and qualitative methods. Research process and design. Appropriate approaches for diverse research questions. Research ethics, sources of data, sampling, measurement, data collection, initial data analysis techniques. Prerequisite: HSCI 130 with a minimum grade of C-. Corequisite: STAT 201 or 203 or 205. Students with credit for HSCI 307 may not take this course for further credit.

Prepares students at a foundational level to evaluate and critique conclusions drawn from epidemiological research. Students will also experience the value and limitations of epidemiology as a tool for researching health and disease in populations. Prerequisite: 30 units, including HSCI 130 with a minimum grade of C-, or permission of the instructor. Students with credit for HSCI 330 may not take this course for further credit.

This course is a continuation of STAT 270. Review of probability models. Procedures for statistical inference using survey results and experimental data. Statistical model building. Elementary design of experiments. Regression methods. Introduction to categorical data analysis. Prerequisite: STAT 270 and one of MATH 152, MATH 155, or MATH 158, all with a minimum grade of C-. Quantitative.

A presentation of the problems commonly arising in numerical analysis and scientific computing and the basic methods for their solutions. Prerequisite: MATH 152 or 155 or 158, and MATH 232 or 240, and computing experience. Quantitative.

Fourier series, ODE boundary and eigenvalue problems. Separation of variables for the diffusion wave and Laplace/Poisson equations. Polar and spherical co-ordinate systems. Symbolic and numerical computing, and graphics for PDEs. Prerequisite: MATH 260 or MATH 310, with a minimum grade of C-; and one of MATH 251 with a grade of B+, or one of MATH 252 or 254, with a minimum grade of C-. Quantitative.

Sequences and series of functions, topology of sets in Euclidean space, introduction to metric spaces, functions of several variables. Prerequisite: MATH 242 and 251, with a minimum grade of C-. Quantitative.

Functions of a complex variable, differentiability, contour integrals, Cauchy's theorem, Taylor and Laurent expansions, method of residues. Prerequisite: MATH 251 with a minimum grade of C-. Students with credit for MATH 424 may not take this course for further credit. Quantitative.

First-order linear equations, the method of characteristics. The wave equation. Harmonic functions, the maximum principle, Green's functions. The heat equation. Distributions and transforms. Higher dimensional eigenvalue problems. An introduction to nonlinear equations. Burgers' equation and shock waves. Prerequisite: (MATH 260 or MATH 310) and one of MATH 314, MATH 320, MATH 322, PHYS 384, all with a minimum grade of C-. An alternative to the above prerequisite is both of (MATH 252 or MATH 254) and (MATH 260 or MATH 310), both with grades of at least A-. Quantitative.

An introduction to probability from the rigorous point of view. Random variables. Generating functions. Convergence of random variables. The strong law of large numbers and the central limit theorem. Stochastic processes. Stationary process and martingales. Prerequisite: MATH 242 and (MATH 348 or STAT 380), all with a minimum grade of C-.

Incompressible fluid flow phenomena: kinematics and equations of motion, viscous flow and boundary layer theory, potential flow, water waves. Aerodynamics. Prerequisite: One of MATH 314, MATH 418, PHYS 384, with a minimum grade of C-. An alternative to the above prerequisite is both of MATH 251 and (MATH 260 or MATH 310), both with grades of at least B+. Quantitative.

Stability and bifurcation in continuous and discrete dynamical systems, with applications. The study of the local and global behaviour of linear and nonlinear systems, including equilibria and periodic orbits, phase plane analysis, conservative systems, limit cycles, the Poincare-Bendixson theorem, Hopf bifurcation and an introduction to chaos. Prerequisite: MATH 260 or MATH 310, with a minimum grade of C-. Quantitative.

An exploration of the mathematics of data science. Analysis of the foundations of algorithms currently used in the field. Potential topics to be covered include: machine learning, compressed sensing, clustering, randomized numerical linear algebra, complex networks and random graph models. Students may repeat this course for further credit under a different topic. Prerequisite: MATH 242, MATH 240 or MATH 232 and STAT 270, all with a minimum grade of C-.

Data structures and algorithms for mathematical objects. Topics include long integer arithmetic, computing polynomial greatest common divisors, the fast Fourier transform, Hensel's lemma and p-adic methods, differentiation and simplification of formulae, and polynomial factorization. Students will use a computer algebra system such as Maple for calculations and programming. Prerequisite: CMPT 307 or ((MATH 340 or MATH 342) and (CMPT 225 or MACM 204)). Quantitative.

Development of numerical methods for solving linear algebra problems at the heart of many scientific computing problems. Mathematical foundations for the use, implementation and analysis of the algorithms used for solving many optimization problems and differential equations. Prerequisite: MATH 251, MACM 316, programming experience. Quantitative.

The numerical solution of ordinary differential equations and elliptic, hyperbolic and parabolic partial differential equations will be considered. Prerequisite: (MATH 260 or MATH 310) and MACM 316. Quantitative.

An introductory treatment of quantum computing with an emphasis on quantum algorithms. Topics include the gate model of quantum computation focusing on the design and implementation of quantum algorithms. Basic knowledge of algorithms and complexity will be an asset, but not required. No prior knowledge of physics or quantum mechanics is necessary, only a solid background in linear algebra. Prerequisite: MATH 232 or MATH 240, with a minimum grade of C-. Students with credit for CMPT 476 may not take this course for further credit. Students who have taken CMPT 409 in Summer 2020 and 2021 under the title "Intro to Quantum Computing" may not take this course for further credit.

An introductory treatment of quantum computing with an emphasis on quantum algorithms. Topics include the gate model of quantum computation focusing on the design and implementation of quantum algorithms. Basic knowledge of algorithms and complexity will be an asset, but not required. No prior knowledge of physics or quantum mechanics is necessary, only a solid background in linear algebra. Prerequisite: MATH 232 or MATH 240, with a minimum grade of C-. Students with credit for MACM 476 may not take this course for further credit.

Linear programming modelling. The simplex method and its variants. Duality theory. Post-optimality analysis. Applications and software. Additional topics may include: game theory, network simplex algorithm, and convex sets. Prerequisite: MATH 150, 151, 154, or 157 and MATH 240 or 232, all with a minimum grade of C-. Quantitative.

Theoretical and computational methods for investigating the minimum of a function of several real variables with and without inequality constraints. Applications to operations research, model fitting, and economic theory. Prerequisite: MATH 232 or 240, and 251, all with a minimum grade of C-. Quantitative.

Linear Algebra. Vector space and matrix theory. Prerequisite: MATH 340 or 332, with a minimum grade of C- or permission of the instructor. Students with credit for MATH 438 may not take this course for further credit. Quantitative.

Structures and algorithms, generating elementary combinatorial objects, counting (integer partitions, set partitions, Catalan families), backtracking algorithms, branch and bound, heuristic search algorithms. Prerequisite: MACM 201 with a minimum grade of C-. Recommended: Knowledge of a programming language. Quantitative.

Fundamental concepts, trees and distances, matchings and factors, connectivity and paths, network flows, integral flows. Prerequisite: MACM 201 with a minimum grade of C-. Quantitative.

Modelling of real-life systems as Markov chains, including transient behaviour, limiting behaviour and classification of states, and using the exponential distribution and Poisson processes. Computational topics include generating and sampling random numbers, combinatorial objects and probability functions. Applications may include queueing systems, chemical kinetics, infectious diseases, and statistical physics. Prerequisite: STAT 270 and (MATH 232 or MATH 240), all with a minimum grade of C-. Strongly Recommended: Experience with a computing platform such as R, MATLAB, or Python. Quantitative.

Key ideas and mathematical methods used in applications of mathematics to various biological, ecological, physiological, and medical problems. The course derives, interprets, solves and simulates models of biological systems. Topics could include population models, evolution from trait and genetic perspectives and qualitative analysis of ODEs. Prerequisite: MATH 260 with a minimum grade of C- or (MATH 155 with a minimum grade of A- and BISC 204 with a minimum grade of C-). Corequisite: BISC 204 may be taken as a corequisite. Strongly Recommended: Experience with a computing platform such as R, MATLAB, or Python. Quantitative.

Convergence in Euclidean spaces, Fourier series and their convergence, Legendre polynomials, Hermite and Laguerre polynomials. Prerequisite: MATH 232 or 240 and one of MATH 314, 320, 322, PHYS 384, all with a minimum grade of C-. Students with credit for MATH 420 or MATH 719 may not complete this course for further credit. Quantitative.

Metric spaces, normed vector spaces, measure and integration, an introduction to functional analysis. Prerequisite: MATH 320 with a minimum grade of C-. Quantitative.

An introduction to probability from the rigorous point of view. Random variables. Generating functions. Convergence of random variables. The strong law of large numbers and the central limit theorem. Stochastic processes. Stationary process and martingales. Prerequisite: MATH 242 and (MATH 348 or STAT 380), all with a minimum grade of C-.

Incompressible fluid flow phenomena: kinematics and equations of motion, viscous flow and boundary layer theory, potential flow, water waves. Aerodynamics. Prerequisite: One of MATH 314, MATH 418, PHYS 384, with a minimum grade of C-. An alternative to the above prerequisite is both of MATH 251 and (MATH 260 or MATH 310), both with grades of at least B+. Quantitative.

Stability and bifurcation in continuous and discrete dynamical systems, with applications. The study of the local and global behaviour of linear and nonlinear systems, including equilibria and periodic orbits, phase plane analysis, conservative systems, limit cycles, the Poincare-Bendixson theorem, Hopf bifurcation and an introduction to chaos. Prerequisite: MATH 260 or MATH 310, with a minimum grade of C-. Quantitative.

Methods and applications of mathematical models in biology, focusing on understanding, analyzing, and applying scientific literature using models and integrating real data. Topics may include parameter estimation in biological models, stochastic simulation of disease outbreaks, age structured population models, and others. Course may be repeated for credit under a different topic. Prerequisite: MATH 360 and (MATH 348 or STAT 380), both with a minimum grade of C-. Corequisite: MATH 348 or STAT 380 may be taken as a corequisite. Strongly Recommended: Experience with a computing platform such as R, MATLAB, or Python.

A survey of contemporary methods and applications of discrete mathematical models focusing on graphs, networks, and trees in evolutionary biology, ecology, and epidemiology. Using discrete models and integrating real data, students will focus on understanding, analyzing, and applying recent scientific literature. Course may be repeated for credit under a different topic. Prerequisite: One of MATH 343, MATH 345, MATH 360, with a minimum grade of C-. Strongly Recommended: Experience with a computing platform such as R, MATLAB, or Python.

An exploration of the mathematics of data science. Analysis of the foundations of algorithms currently used in the field. Potential topics to be covered include: machine learning, compressed sensing, clustering, randomized numerical linear algebra, complex networks and random graph models. Students may repeat this course for further credit under a different topic. Prerequisite: MATH 242, MATH 240 or MATH 232 and STAT 270, all with a minimum grade of C-.

The topics included in this course will vary from term to term depending on faculty availability and student interest. Prerequisite: Will be specified according to the particular topic or topics offered under this course number.

Examines central philosophical issues related to mathematics. Topics may include the metaphysical status of mathematical entities, mathematical knowledge, set theory and others. Prerequisite: One of PHIL 110, 210, 314, 315 or MACM 101. Writing.

Central forces, rigid body motion, small oscillations. Lagrangian and Hamiltonian formulations of mechanics. Prerequisite: PHYS 384 or permission of the department. Non-physics majors may enter with MATH 252; MATH 260 or MATH 310; PHYS 211. All prerequisite courses require a minimum grade of C-. Quantitative.

Review of discrete and continuous probability models and relationships between them. Exploration of conditioning and conditional expectation. Markov chains. Random walks. Continuous time processes. Poisson process. Markov processes. Gaussian processes. Prerequisite: STAT 330, or all of: STAT 285, MATH 208W, and MATH 251, all with a minimum grade of C-. Quantitative.

A presentation of the problems commonly arising in numerical analysis and scientific computing and the basic methods for their solutions. Prerequisite: MATH 152 or 155 or 158, and MATH 232 or 240, and computing experience. Quantitative.

Modelling of real-life systems as Markov chains, including transient behaviour, limiting behaviour and classification of states, and using the exponential distribution and Poisson processes. Computational topics include generating and sampling random numbers, combinatorial objects and probability functions. Applications may include queueing systems, chemical kinetics, infectious diseases, and statistical physics. Prerequisite: STAT 270 and (MATH 232 or MATH 240), all with a minimum grade of C-. Strongly Recommended: Experience with a computing platform such as R, MATLAB, or Python. Quantitative.

Key ideas and mathematical methods used in applications of mathematics to various biological, ecological, physiological, and medical problems. The course derives, interprets, solves and simulates models of biological systems. Topics could include population models, evolution from trait and genetic perspectives and qualitative analysis of ODEs. Prerequisite: MATH 260 with a minimum grade of C- or (MATH 155 with a minimum grade of A- and BISC 204 with a minimum grade of C-). Corequisite: BISC 204 may be taken as a corequisite. Strongly Recommended: Experience with a computing platform such as R, MATLAB, or Python. Quantitative.

Fourier series, ODE boundary and eigenvalue problems. Separation of variables for the diffusion wave and Laplace/Poisson equations. Polar and spherical co-ordinate systems. Symbolic and numerical computing, and graphics for PDEs. Prerequisite: MATH 260 or MATH 310, with a minimum grade of C-; and one of MATH 251 with a grade of B+, or one of MATH 252 or 254, with a minimum grade of C-. Quantitative.

Sequences and series of functions, topology of sets in Euclidean space, introduction to metric spaces, functions of several variables. Prerequisite: MATH 242 and 251, with a minimum grade of C-. Quantitative.

Functions of a complex variable, differentiability, contour integrals, Cauchy's theorem, Taylor and Laurent expansions, method of residues. Prerequisite: MATH 251 with a minimum grade of C-. Students with credit for MATH 424 may not take this course for further credit. Quantitative.

First-order linear equations, the method of characteristics. The wave equation. Harmonic functions, the maximum principle, Green's functions. The heat equation. Distributions and transforms. Higher dimensional eigenvalue problems. An introduction to nonlinear equations. Burgers' equation and shock waves. Prerequisite: (MATH 260 or MATH 310) and one of MATH 314, MATH 320, MATH 322, PHYS 384, all with a minimum grade of C-. An alternative to the above prerequisite is both of (MATH 252 or MATH 254) and (MATH 260 or MATH 310), both with grades of at least A-. Quantitative.

Methods and applications of mathematical models in biology, focusing on understanding, analyzing, and applying scientific literature using models and integrating real data. Topics may include parameter estimation in biological models, stochastic simulation of disease outbreaks, age structured population models, and others. Course may be repeated for credit under a different topic. Prerequisite: MATH 360 and (MATH 348 or STAT 380), both with a minimum grade of C-. Corequisite: MATH 348 or STAT 380 may be taken as a corequisite. Strongly Recommended: Experience with a computing platform such as R, MATLAB, or Python.

A survey of contemporary methods and applications of discrete mathematical models focusing on graphs, networks, and trees in evolutionary biology, ecology, and epidemiology. Using discrete models and integrating real data, students will focus on understanding, analyzing, and applying recent scientific literature. Course may be repeated for credit under a different topic. Prerequisite: One of MATH 343, MATH 345, MATH 360, with a minimum grade of C-. Strongly Recommended: Experience with a computing platform such as R, MATLAB, or Python.

Linear programming modelling. The simplex method and its variants. Duality theory. Post-optimality analysis. Applications and software. Additional topics may include: game theory, network simplex algorithm, and convex sets. Prerequisite: MATH 150, 151, 154, or 157 and MATH 240 or 232, all with a minimum grade of C-. Quantitative.

Theoretical and computational methods for investigating the minimum of a function of several real variables with and without inequality constraints. Applications to operations research, model fitting, and economic theory. Prerequisite: MATH 232 or 240, and 251, all with a minimum grade of C-. Quantitative.

Fourier series, ODE boundary and eigenvalue problems. Separation of variables for the diffusion wave and Laplace/Poisson equations. Polar and spherical co-ordinate systems. Symbolic and numerical computing, and graphics for PDEs. Prerequisite: MATH 260 or MATH 310, with a minimum grade of C-; and one of MATH 251 with a grade of B+, or one of MATH 252 or 254, with a minimum grade of C-. Quantitative.

Sequences and series of functions, topology of sets in Euclidean space, introduction to metric spaces, functions of several variables. Prerequisite: MATH 242 and 251, with a minimum grade of C-. Quantitative.

Functions of a complex variable, differentiability, contour integrals, Cauchy's theorem, Taylor and Laurent expansions, method of residues. Prerequisite: MATH 251 with a minimum grade of C-. Students with credit for MATH 424 may not take this course for further credit. Quantitative.

First-order linear equations, the method of characteristics. The wave equation. Harmonic functions, the maximum principle, Green's functions. The heat equation. Distributions and transforms. Higher dimensional eigenvalue problems. An introduction to nonlinear equations. Burgers' equation and shock waves. Prerequisite: (MATH 260 or MATH 310) and one of MATH 314, MATH 320, MATH 322, PHYS 384, all with a minimum grade of C-. An alternative to the above prerequisite is both of (MATH 252 or MATH 254) and (MATH 260 or MATH 310), both with grades of at least A-. Quantitative.

An introduction to probability from the rigorous point of view. Random variables. Generating functions. Convergence of random variables. The strong law of large numbers and the central limit theorem. Stochastic processes. Stationary process and martingales. Prerequisite: MATH 242 and (MATH 348 or STAT 380), all with a minimum grade of C-.

Stability and bifurcation in continuous and discrete dynamical systems, with applications. The study of the local and global behaviour of linear and nonlinear systems, including equilibria and periodic orbits, phase plane analysis, conservative systems, limit cycles, the Poincare-Bendixson theorem, Hopf bifurcation and an introduction to chaos. Prerequisite: MATH 260 or MATH 310, with a minimum grade of C-. Quantitative.

Methods and applications of mathematical models in biology, focusing on understanding, analyzing, and applying scientific literature using models and integrating real data. Topics may include parameter estimation in biological models, stochastic simulation of disease outbreaks, age structured population models, and others. Course may be repeated for credit under a different topic. Prerequisite: MATH 360 and (MATH 348 or STAT 380), both with a minimum grade of C-. Corequisite: MATH 348 or STAT 380 may be taken as a corequisite. Strongly Recommended: Experience with a computing platform such as R, MATLAB, or Python.

A survey of contemporary methods and applications of discrete mathematical models focusing on graphs, networks, and trees in evolutionary biology, ecology, and epidemiology. Using discrete models and integrating real data, students will focus on understanding, analyzing, and applying recent scientific literature. Course may be repeated for credit under a different topic. Prerequisite: One of MATH 343, MATH 345, MATH 360, with a minimum grade of C-. Strongly Recommended: Experience with a computing platform such as R, MATLAB, or Python.

An exploration of the mathematics of data science. Analysis of the foundations of algorithms currently used in the field. Potential topics to be covered include: machine learning, compressed sensing, clustering, randomized numerical linear algebra, complex networks and random graph models. Students may repeat this course for further credit under a different topic. Prerequisite: MATH 242, MATH 240 or MATH 232 and STAT 270, all with a minimum grade of C-.

The topics included in this course will vary from term to term depending on faculty availability and student interest. Prerequisite: Will be specified according to the particular topic or topics offered under this course number.

The phenomenon of organic evolution, and the major processes leading to changes in allele frequencies over time, namely mutation, migration, genetic drift and natural selection. Topics include evolutionary genetics, adaptation, sexual selection, the origin of life, speciation, and the major evolutionary trends over geological time. Prerequisite: BISC 202 with a minimum grade of C-. Recommended: BISC 204.

Introduces environmental scientists to application of modern data analysis methods. This course covers sampling, experimental design, and the analysis of quantitative data collected in the course of environmental monitoring, assessment and restoration programs. Students will be introduced and gain experience with the statistical programming language R. Prerequisite: GEOG 251, or one of STAT 100, 201, 203, 205 or 270 or permission of the instructor.

Human variation and human health in the context of population genetics, epidemiology, demography, and human evolution. Prerequisite: BISC 202 with a minimum grade of C-.

As a follow-up to HSCI 230, this course aims to build further literacy of fundamental epidemiological concepts and approaches, and skills to apply basic quantitative measures to describe and evaluate public health issues as well as communicate the magnitude and potential determinants of those issues. Prerequisite: HSCI 230, and STAT 201 or 203 or 205, all with a minimum grade of C-.

Regression and data analysis techniques for health research. Practical approaches to linear and logistic regression, multivariable modelling, interaction, variable selection, confounding, and measures of association. Computer-based laboratory exercises using statistical software applied to health datasets. Prerequisite: STAT 302 or STAT 305, with a minimum grade of C-. Recommended: HSCI 230.

Students receive hands-on experience in the construction and analysis of computer simulation models of environmental and ecological systems and problems. Prerequisite: REM 100 or EVSC 100; BISC 204 or GEOG 215 or REM 211; STAT 201 or STAT 203 or STAT 205 or GEOG 251 or equivalent; and 60 units. Recommended: REM 225. Quantitative.

Intermediate statistical techniques for the health sciences. Review of introductory concepts in statistics and probability including hypothesis testing, estimation and confidence intervals for means and proportions. Contingency tables and the analysis of multiple 2x2 tables. Correlation and regression. Multiple regression and model selection. Logistic regression and odds ratios. Basic concepts in survival analysis. This course may not be used to satisfy the upper division requirements of the following programs: statistics major, statistics honours, actuarial science major, and actuarial science honours. Prerequisite: One of STAT 201, STAT 203, STAT 205, STAT 270, BUS 232, or ECON 233, with a minimum grade of C-. Students who have taken STAT 350 first may not then take this course for further credit. Quantitative.

Review of probability and distributions. Multivariate distributions. Distributions of functions of random variables. Limiting distributions. Inference. Sufficient statistics for the exponential family. Maximum likelihood. Bayes estimation, Fisher information, limiting distributions of MLEs. Likelihood ratio tests. Prerequisite: STAT 285, MATH 251, and one of MATH 232 or MATH 240, all with a minimum grade of C-. Quantitative.

Introduction to linear time series analysis including moving average, autoregressive and ARIMA models, estimation, data analysis, forecasting errors and confidence intervals, conditional and unconditional models, and seasonal models. Prerequisite: STAT 260 and one of STAT 285 or STAT 302 or STAT 305 or ECON 333 or equivalent, with a minimum grade of C-. Students with credit for ECON 484 may not take this course for further credit. Quantitative.