2010-2011
This is a challenging program providing students with a solid training in both computer science and statistics suitable for entry into graduate school in either discipline.
Students may complete this program with a minimum of 76 credits or a maximum of 79 credits depending on whether or not they are exempt from taking COMP 202.
Students entering the Joint Honours in Statistics and Computer Science are normally expected to have completed the courses below or their equivalents. Otherwise, they will be required to make up any deficiencies in these courses over and above the 76 - 79 credits of courses in the program.
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; quadratic loci in two and three dimensions.
Terms: Fall 2010, Winter 2011, Summer 2011
Instructors: Djivede Kelome, William J Anderson, James G Loveys, Shahab Shahabi, Adam Clay (Fall) Djivede Kelome, William J Anderson (Winter) Karol Palka (Summer)
Prerequisite: a course in functions
Restriction: Not open to students who have taken MATH 221 or CEGEP objective 00UQ or equivalent.
Restriction Note B: Not open to students who have taken or are taking MATH 123, MATH 130 or MATH 131, except by permission of the Department of Mathematics and Statistics.
Mathematics & Statistics (Sci) : Review of functions and graphs. Limits, continuity, derivative. Differentiation of elementary functions. Antidifferentiation. Applications.
Terms: Fall 2010, Winter 2011, Summer 2011
Instructors: Stephen W Drury, Sidney Trudeau, Shahab Shahabi (Fall) Axel W Hundemer (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 or MATH 130 or MATH 131, except by permission of the Department of Mathematics and Statistics
Each Tutorial section is enrolment limited
Mathematics & Statistics (Sci) : The definite integral. Techniques of integration. Applications. Introduction to sequences and series.
Terms: Fall 2010, Winter 2011, Summer 2011
Instructors: Sidney Trudeau (Fall) Neville G F Sancho, Stephen W Drury, Sidney Trudeau (Winter)
Restriction: Not open to students who have taken MATH 121 or CEGEP objective 00UP or equivalent
Restriction Note B: Not open to students who have taken or are taking MATH 122 or MATH 130 or MATH 131, except by permission of the Department of Mathematics and Statistics.
Each Tutorial section is enrolment limited
* Students who have sufficient knowledge in a programming language are not required to take COMP 202.
** Students take either MATH 251 or MATH 247, but not both.
Computer Science (Sci) : Overview of components of microcomputers, the internet design and implementation of programs using a modern high-level language, an introduction to modular software design and debugging. Programming concepts are illustrated using a variety of application areas.
Terms: Fall 2010, Winter 2011, Summer 2011
Instructors: Mathieu Petitpas, Maja Frydrychowicz (Fall) Maja Frydrychowicz, Daniel Pomerantz (Winter) Daniel Pomerantz (Summer)
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
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 2010, Winter 2011
Instructors: Joseph P Vybihal (Fall) Joseph P Vybihal, Gregory L Dudek (Winter)
Computer Science (Sci) : An introduction to the design of computer algorithms, including basic data structures, analysis of algorithms, and establishing correctness of programs. Overview of topics in computer science.
Terms: Fall 2010, Winter 2011
Instructors: Doina Precup (Fall) Michael Langer (Winter)
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 2011
Instructors: Luc P Devroye (Winter)
3 hours
Restrictions: Open only to students registered in following programs: Honours in Computer Science, Joint Honours in Mathematics and Computer Science, Honours in Applied Mathematics, Honours in Mathematics. Not open to students who have taken or are taking COMP 251.
Note: COMP 252 can be used instead of COMP 251 to satisfy prerequisites.
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 2010, Winter 2011
Instructors: Joseph P Vybihal (Fall) Kaleem Siddiqi (Winter)
3 hours
Corequisite: COMP 206.
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 2010, Winter 2011
Instructors: Brigitte Pientka (Fall) Jesse Doherty (Winter)
Computer Science (Sci) : Mathematical models of computers, finite automata, Turing machines, counter machines, push-down machines, computational complexity.
Terms: Fall 2010
Instructors: Hamed Hatami (Fall)
3 hours
Prerequisite: COMP 251.
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: Fall 2010
Instructors: Mohit Singh (Fall)
Mathematics & Statistics (Sci) : Sets, functions and relations. Methods of proof. Complex numbers. Divisibility theory for integers and modular arithmetic. Divisibility theory for polynomials. Rings, ideals and quotient rings. Fields and construction of fields from polynomial rings. Groups, subgroups and cosets; group actions on sets.
Terms: Fall 2010
Instructors: Heekyoung Hahn (Fall)
Fall
3 hours lecture; 1 hour tutorial
Prerequisite: MATH 133 or equivalent
Mathematics & Statistics (Sci) : A rigorous presentation of sequences and of real numbers and basic properties of continuous and differentiable functions on the real line.
Terms: Fall 2010
Instructors: Reem Adel Yassawi (Fall)
Fall
Prerequisite: MATH 141
Mathematics & Statistics (Sci) : Matrix algebra, determinants, systems of linear equations. Abstract vector spaces, inner product spaces, Fourier series. Linear transformations and their matrix representations. Eigenvalues and eigenvectors, diagonalizable and defective matrices, positive definite and semidefinite matrices. Quadratic and Hermitian forms, generalized eigenvalue problems, simultaneous reduction of quadratic forms. Applications.
Terms: Winter 2011
Instructors: Axel W Hundemer (Winter)
Mathematics & Statistics (Sci) : Partial derivatives; implicit functions; Jacobians; maxima and minima; Lagrange multipliers. Scalar and vector fields; orthogonal curvilinear coordinates. Multiple integrals; arc length, volume and surface area. Line integrals; Green's theorem; the divergence theorem. Stokes' theorem; irrotational and solenoidal fields; applications.
Terms: Fall 2010
Instructors: Pengfei Guan (Fall)
Mathematics & Statistics (Sci) : Linear equations over a field. Introduction to vector spaces. Linear maps and their matrix representation. Determinants. Canonical forms. Duality. Bilinear and quadratic forms. Real and complex inner product spaces. Diagonalization of self-adjoint operators.
Terms: Winter 2011
Instructors: James G Loveys (Winter)
Mathematics & Statistics (Sci) : Series of functions including power series. Riemann integration in one variable. Elementary functions.
Terms: Winter 2011
Instructors: Vojkan Jaksic (Winter)
Winter
Prerequisites: MATH 242 or permission of the Department
Mathematics & Statistics (Sci) : Basic combinatorial probability. Introductory distribution theory of univariate and multivariate distributions with special reference to the Binomial, Poisson, Gamma and Normal distributions. Characteristic functions. Weak law of large numbers. Central limit theorem.
Terms: Fall 2010
Instructors: Johanna Neslehova (Fall)
Mathematics & Statistics (Sci) : Data analysis. Estimation and hypothesis testing. Power of tests. Likelihood ratio criterion. The chi-squared goodness of fit test. Introduction to regression analysis and analysis of variance.
Terms: Winter 2011
Instructors: Masoud Asgharian-Dastenaei (Winter)
Mathematics & Statistics (Sci) : This course consists of the lectures of MATH 423 but will be assessed at the 500 level.
Terms: Fall 2010
Instructors: Abbas Khalili Mahmoudabadi (Fall)
15 credits in Mathematics selected as follows:
3 credits selected from:
Mathematics & Statistics (Sci) : Error analysis. Numerical solutions of equations by iteration. Interpolation. Numerical differentiation and integration. Introduction to numerical solutions of differential equations.
Terms: This course is not scheduled for the 2010-2011 academic year.
Instructors: There are no professors associated with this course for the 2010-2011 academic year.
Mathematics & Statistics (Sci) : The course consists of the lectures of MATH 327 plus additional work involving theoretical assignments and/or a project. The final examination for this course may be different from that of MATH 327.
Terms: Winter 2011
Instructors: Antony Raymond Humphries (Winter)
At least 8 credits selected from:
Mathematics & Statistics (Sci) : Modern discrete data analysis. Exponential families, orthogonality, link functions. Inference and model selection using analysis of deviance. Shrinkage (Bayesian, frequentist viewpoints). Smoothing. Residuals. Quasi-likelihood. Sliced inverse regression. Contingency tables: logistic regression, log-linear models. Censored data. Applications to current problems in medicine, biological and physical sciences. GLIM, S, software.
Terms: Winter 2011
Instructors: David Stephens (Winter)
Mathematics & Statistics (Sci) : Distribution free procedures for 2-sample problem: Wilcoxon rank sum, Siegel-Tukey, Smirnov tests. Shift model: power and estimation. Single sample procedures: Sign, Wilcoxon signed rank tests. Nonparametric ANOVA: Kruskal-Wallis, Friedman tests. Association: Spearman's rank correlation, Kendall's tau. Goodness of fit: Pearson's chi-square, likelihood ratio, Kolmogorov-Smirnov tests. Statistical software packages used.
Terms: Fall 2010
Instructors: Christian Genest (Fall)
Mathematics & Statistics (Sci) : Simple random sampling, domains, ratio and regression estimators, superpopulation models, stratified sampling, optimal stratification, cluster sampling, sampling with unequal probabilities, multistage sampling, complex surveys, nonresponse.
Terms: This course is not scheduled for the 2010-2011 academic year.
Instructors: There are no professors associated with this course for the 2010-2011 academic year.
Mathematics & Statistics (Sci) : Probability and distribution theory (univariate and multivariate). Exponential families. Laws of large numbers and central limit theorem.
Terms: Fall 2010
Instructors: Johanna Neslehova (Fall)
Fall
Prerequisite: MATH 357 or equivalent
Mathematics & Statistics (Sci) : Sampling theory (including large-sample theory). Likelihood functions and information matrices. Hypothesis testing, estimation theory. Regression and correlation theory.
Terms: Winter 2011
Instructors: Christian Genest (Winter)
Winter
Prerequisite: MATH 556
The remaining Mathematics credits selected from:
** MATH 578 and COMP 540 cannot both be taken for program credit.
Mathematics & Statistics (Sci) : Graph models. Graph connectivity, planarity and colouring. Extremal graph theory. Matroids. Enumerative combinatorics and listing.
Terms: Winter 2011
Instructors: Bruce Alan Reed (Winter)
Mathematics & Statistics (Sci) : Seminar in Mathematical Problem Solving. The problems considered will be of the type that occur in the Putnam competition and in other similar mathematical competitions.
Terms: Fall 2010
Instructors: James G Loveys (Fall)
Prerequisite: Enrolment in a math related program or permission of the instructor. Requires departmental approval.
Prerequisite: Enrolment in a math related program or permission of the instructor.
Mathematics & Statistics (Sci) : Introduction to metric spaces. Multivariable differential calculus, implicit and inverse function theorems.
Terms: Fall 2010
Instructors: Dmitry Jakobson (Fall)
Fall
Prerequisite: MATH 255 or equivalent
Mathematics & Statistics (Sci) : Lebesque measure, integration and Fubini's theorem. Abstract measure and integration. Convergence theorems. Introduction to Hilbert spaces, L_2 spaces, Fourier series. Fourier integrals (if time allows).
Terms: Winter 2011
Instructors: Dmitry Jakobson (Winter)
Winter
Prerequisite: MATH 354 or equivalent.
Mathematics & Statistics (Sci) : Development, analysis and effective use of numerical methods to solve problems arising in applications. Topics include direct and iterative methods for the solution of linear equations (including preconditioning), eigenvalue problems, interpolation, approximation, quadrature, solution of nonlinear systems.
Terms: Fall 2010
Instructors: Jean-Christophe Nave (Fall)
15 credits in Computer Science selected as follows:
At least 6 credits selected from:
Computer Science (Sci) : Information Theory. Huffman, arithmetic and dictionary codes. Context Modelling. Lossy compression and quantization. Signal processing. Applications to text, image, speech, audio and video data.
Terms: This course is not scheduled for the 2010-2011 academic year.
Instructors: There are no professors associated with this course for the 2010-2011 academic year.
Computer Science (Sci) : Introduction to search methods. Knowledge representation using logic and probability. Planning and decision making under uncertainty. Introduction to machine learning.
Terms: Winter 2011
Instructors: Joelle Pineau (Winter)
Computer Science (Sci) : Application of computer science techniques to problems arising in biology and medicine, techniques for modeling evolution, aligning molecular sequences, predicting structure of a molecule and other problems from computational biology.
Terms: Fall 2010
Instructors: Jerome Waldispuhl (Fall)
Computer Science (Sci) : Fundamental tools from probability are used to analyze algorithms. Notions covered included independence, generating functions, probability inequalities, random walks and Markov chains. Analysis of probabilistic recurrences, Las Vegas algorithms, randomized approximation algorithms, random sampling methods, Monte Carlo techniques and algorithms for combinatorial search and graph theoretic problems.
Terms: This course is not scheduled for the 2010-2011 academic year.
Instructors: There are no professors associated with this course for the 2010-2011 academic year.
Computer Science (Sci) : Belief networks, Utility theory, Markov Decision Processes and Learning Algorithms.
Terms: This course is not scheduled for the 2010-2011 academic year.
Instructors: There are no professors associated with this course for the 2010-2011 academic year.
Computer Science (Sci) : Designing and programming reliable numerical algorithms. Stability of algorithms and condition of problems. Reliable and efficient algorithms for solution of equations, linear least squares problems, the singular value decomposition, the eigenproblem and related problems. Perturbation analysis of problems. Algorithms for structured matrices.
Terms: Winter 2011
Instructors: Xiao-Wen Chang (Winter)
Computer Science (Sci) : This course presents an in-depth study of modern cryptography and data security. The basic information theoretic and computational properties of classical and modern cryptographic systems are presented, followed by a cryptanalytic examination of several important systems. We will study the applications of cryptography to the security of systems.
Terms: Fall 2010
Instructors: Claude Crepeau (Fall)
Computer Science (Sci) : Algorithmic and structural approaches in combinatorial optimization with a focus upon theory and applications. Topics include: polyhedral methods, network optimization, the ellipsoid method, graph algorithms, matroid theory and submodular functions.
Terms: This course is not scheduled for the 2010-2011 academic year.
Instructors: There are no professors associated with this course for the 2010-2011 academic year.
Computer Science (Sci) : This course examines computational problems related to gene regulation at the mRNA and protein levels. With respect to mRNA expression, topics include microarray analysis, SNP detection, and the inference of genetic networks. With respect to protein expression, topics include peptide sequencing, peptide identification, and the interpretation of interaction maps.
Terms: This course is not scheduled for the 2010-2011 academic year.
Instructors: There are no professors associated with this course for the 2010-2011 academic year.
3 hours
Prerequisite: COMP 462.
Computer Science (Sci) : Use of computer in solving problems in discrete optimization. Linear programming and extensions. Network simplex method. Applications of linear programming. Vertex enumeration. Geometry of linear programming. Implementation issues and robustness. Students will do a project on an application of their choice.
Terms: Fall 2010
Instructors: Guyslain Naves (Fall)
Computer Science (Sci) : Formulation, solution and applications of integer programs. Branch and bound, cutting plane, and column generation algorithms. Combinatorial optimization. Polyhedral methods. A large emphasis will be placed on modelling. Students will select and present a case study of an application of integer programming in an area of their choice.
Terms: This course is not scheduled for the 2010-2011 academic year.
Instructors: There are no professors associated with this course for the 2010-2011 academic year.
The remaining Computer Science credits are selected from COMP courses at the 300-level or above excluding COMP 396 and COMP 431.