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Honours Statistics and Computer Science (79 credits)

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Offered by: Mathematics and Statistics     Degree: Bachelor of Science

Program Requirements

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.

Program Prerequisites

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.

  • MATH 133 Linear Algebra and Geometry (3 credits)

    Offered by: Mathematics and Statistics (Faculty of Science)

    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; quadratic loci in two and three dimensions.

    Terms: Fall 2019, Winter 2020, Summer 2020

    Instructors: B茅langer-Rioux, Rosalie; Omar, Zayd; Albanese, Michael (Fall) Ghaswala, Tyrone; Hurtubise, Jacques Claude (Winter) Sicca Gon莽alves, Vladmir (Summer)

    • 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, MATH 130 or MATH 131, 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)

    Offered by: Mathematics and Statistics (Faculty of Science)

    Overview

    Mathematics & Statistics (Sci) : Review of functions and graphs. Limits, continuity, derivative. Differentiation of elementary functions. Antidifferentiation. Applications.

    Terms: Fall 2019, Winter 2020, Summer 2020

    Instructors: Trudeau, Sidney; Negrini, Isabella; Walker, Aled (Fall) Fortier, J茅r么me (Winter) Zenz, Peter (Summer)

    • 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

  • MATH 141 Calculus 2 (4 credits)

    Offered by: Mathematics and Statistics (Faculty of Science)

    Overview

    Mathematics & Statistics (Sci) : The definite integral. Techniques of integration. Applications. Introduction to sequences and series.

    Terms: Fall 2019, Winter 2020, Summer 2020

    Instructors: Haris, Asad; Trudeau, Sidney; Abdenbi, Brahim (Fall) Trudeau, Sidney; Beckman, Erin; Macdonald, Jeremy (Winter) Abdenbi, Brahim; Chinis, Iakovos (Summer)

    • Prerequisites: MATH 139 or MATH 140 or MATH 150.

    • 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

Required Courses (46 credits)

* 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.

  • COMP 202 Foundations of Programming (3 credits) *

    Offered by: Computer Science (Faculty of Science)

    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 2019, Winter 2020, Summer 2020

    Instructors: Patitsas, Elizabeth; Alberini, Giulia (Fall) Alberini, Giulia (Winter) Campbell, Jonathan (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

  • COMP 206 Introduction to Software Systems (3 credits)

    Offered by: Computer Science (Faculty of Science)

    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 2019, Winter 2020

    Instructors: Vybihal, Joseph P (Fall) Vybihal, Joseph P; D'silva, Joseph (Winter)

  • COMP 250 Introduction to Computer Science (3 credits)

    Offered by: Computer Science (Faculty of Science)

    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 non-recursive algorithms (searching and sorting, tree and graph traversal). Abstract data types, inheritance. Selected topics.

    Terms: Fall 2019, Winter 2020

    Instructors: Langer, Michael; Alberini, Giulia (Fall) Alberini, Giulia; Sarrazin Gendron, Roman (Winter)

    • 3 hours

    • Prerequisites: Familiarity with a high level programming language and CEGEP level Math.

    • Students with limited programming experience should take COMP 202 or equivalent before COMP 250. See COMP 202 Course Description for a list of topics.

  • COMP 252 Honours Algorithms and Data Structures (3 credits)

    Offered by: Computer Science (Faculty of Science)

    Overview

    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 2020

    Instructors: Devroye, Luc P (Winter)

    • 3 hours

    • Prerequisite: COMP 250 and either MATH 235 or MATH 240

    • Restrictions: (1) Open only to students in Honours programs. (2) Students cannot receive credit for both COMP 251 and COMP 252.

    • COMP 252 uses basic combinatorial counting methods that are covered in MATH 240 but not in MATH 235. Students who are unfamiliar with these methods should speak with the instructor for guidance.

  • COMP 273 Introduction to Computer Systems (3 credits)

    Offered by: Computer Science (Faculty of Science)

    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 2019, Winter 2020

    Instructors: Vybihal, Joseph P (Fall) Siddiqi, Kaleem; Syed, Tabish (Winter)

  • COMP 302 Programming Languages and Paradigms (3 credits)

    Offered by: Computer Science (Faculty of Science)

    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 2019, Winter 2020

    Instructors: Pientka, Brigitte; Errington, Jacob (Fall) Panangaden, Prakash (Winter)

  • COMP 330 Theory of Computation (3 credits)

    Offered by: Computer Science (Faculty of Science)

    Overview

    Computer Science (Sci) : Finite automata, regular languages, context-free languages, push-down automata, models of computation, computability theory, undecidability, reduction techniques.

    Terms: Fall 2019

    Instructors: Crepeau, Claude (Fall)

  • COMP 362 Honours Algorithm Design (3 credits)

    Offered by: Computer Science (Faculty of Science)

    Overview

    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: Winter 2020

    Instructors: Reed, Bruce Alan (Winter)

    • 3 hours

    • Prerequisite: COMP 252

    • Restriction: Not open to students who have taken or are taking COMP 360.

    • Note: COMP 362 can be used instead of COMP 360 to satisfy prerequisites.

  • MATH 235 Algebra 1 (3 credits)

    Offered by: Mathematics and Statistics (Faculty of Science)

    Overview

    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 2019

    Instructors: Wise, Daniel (Fall)

    • Fall

    • 3 hours lecture; 1 hour tutorial

    • Prerequisite: MATH 133 or equivalent

  • MATH 247 Honours Applied Linear Algebra (3 credits) **

    Offered by: Mathematics and Statistics (Faculty of Science)

    Overview

    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 2020

    Instructors: Hoheisel, Tim (Winter)

    • Winter

    • Prerequisite: MATH 133 or equivalent.

    • Restriction: Intended for Honours Physics and Engineering students

    • Restriction: Not open to students who have taken or are taking MATH 236, MATH 223 or MATH 251

  • MATH 248 Honours Vector Calculus (3 credits)

    Offered by: Mathematics and Statistics (Faculty of Science)

    Overview

    Mathematics & Statistics (Sci) : Partial derivatives and differentiation of functions in several variables; Jacobians; maxima and minima; implicit functions. Scalar and vector fields; orthogonal curvilinear coordinates. Multiple integrals; arc length, volume and surface area. Line and surface integrals; irrotational and solenoidal fields; Green's theorem; the divergence theorem. Stokes' theorem; and applications.

    Terms: Fall 2019

    Instructors: Tsogtgerel, Gantumur (Fall)

    • Fall and Winter and Summer

    • Prerequisites: MATH 133 and MATH 222 or consent of Department.

    • Restriction: Intended for Honours Physics, Computer Science, Physiology and Engineering students.

    • Restriction: Not open to students who have taken or are taking MATH 314 or MATH 358.

  • MATH 251 Honours Algebra 2 (3 credits) **

    Offered by: Mathematics and Statistics (Faculty of Science)

    Overview

    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 2020

    Instructors: Darmon, Henri (Winter)

    • Winter

    • Prerequisites: MATH 235 or permission of the Department

    • Restriction: Not open to students who are taking or have taken MATH 247

  • MATH 255 Honours Analysis 2 (3 credits)

    Offered by: Mathematics and Statistics (Faculty of Science)

    Overview

    Mathematics & Statistics (Sci) : Basic point-set topology, metric spaces: open and closed sets, normed and Banach spaces, H脙露lder and Minkowski inequalities, sequential compactness, Heine-Borel, Banach Fixed Point theorem. Riemann-(Stieltjes) integral, Fundamental Theorem of Calculus, Taylor's theorem. Uniform convergence. Infinite series, convergence tests, power series. Elementary functions.

    Terms: Winter 2020

    Instructors: Guan, Pengfei (Winter)

  • MATH 356 Honours Probability (3 credits)

    Offered by: Mathematics and Statistics (Faculty of Science)

    Overview

    Mathematics & Statistics (Sci) : Sample space, probability axioms, combinatorial probability. Conditional probability, Bayes' Theorem. Distribution theory with special reference to the Binomial, Poisson, and Normal distributions. Expectations, moments, moment generating functions, uni-variate transformations. Random vectors, independence, correlation, multivariate transformations. Conditional distributions, conditional expectation.Modes of stochastic convergence, laws of large numbers, Central Limit Theorem.

    Terms: Fall 2019

    Instructors: Khalili Mahmoudabadi, Abbas (Fall)

    • Fall

    • Prerequisite(s): MATH 243 or MATH 255, and MATH 222 or permission of the Department.

    • Restriction: Not open to students who have taken or are taking MATH 323

  • MATH 357 Honours Statistics (3 credits)

    Offered by: Mathematics and Statistics (Faculty of Science)

    Overview

    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 2020

    Instructors: Neslehova, Johanna (Winter)

    • Winter

    • Prerequisite: MATH 356 or equivalent

    • Restriction: Not open to students who have taken or are taking MATH 324

  • MATH 533 Honours Regression and Analysis of Variance (4 credits)

    Offered by: Mathematics and Statistics (Faculty of Science)

    Overview

    Mathematics & Statistics (Sci) : This course consists of the lectures of MATH 423 but will be assessed at the 500 level.

    Terms: Fall 2019

    Instructors: Yang, Yi (Fall)

    • Prerequisites: MATH 357, MATH 247 or MATH 251.

    • Restriction: Not open to have taken or are taking MATH 423.

    • Note: An additional project or projects assigned by the instructor that require a more detailed treatment of the major results and concepts covered in MATH 423.

Complementary Courses (33 credits)

18 credits in Mathematics selected as follows:

3 credits selected from:

  • MATH 242 Analysis 1 (3 credits)

    Offered by: Mathematics and Statistics (Faculty of Science)

    Overview

    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 2019

    Instructors: Vetois, Jerome (Fall)

    • Fall

    • Prerequisite: MATH 141

    • Restriction(s): Not open to students who are taking or who have taken MATH 254.

  • MATH 254 Honours Analysis 1 (3 credits) *

    Offered by: Mathematics and Statistics (Faculty of Science)

    Overview

    Mathematics & Statistics (Sci) : Properties of R. Cauchy and monotone sequences, Bolzano- Weierstrass theorem. Limits, limsup, liminf of functions. Pointwise, uniform continuity: Intermediate Value theorem. Inverse and monotone functions. Differentiation: Mean Value theorem, L'Hospital's rule, Taylor's Theorem.

    Terms: Fall 2019

    Instructors: Hundemer, Axel W (Fall)

    • Prerequisite(s): MATH 141

    • Restriction(s): Not open to students who are taking or who have taken MATH 242.

* It is strongly recommended that students take MATH 254.

3 credits selected from:

  • MATH 387 Honours Numerical Analysis (3 credits)

    Offered by: Mathematics and Statistics (Faculty of Science)

    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: Winter 2020

    Instructors: Humphries, Antony Raymond (Winter)

    • Taught in alternate years

    • Winter (even years)

    • Prerequisites: MATH 325 or MATH 315, COMP 202 or permission of instructor.

    • Corequisites: MATH 255 or MATH 243.

    • Restriction: Intended primarily for Honours students.

  • MATH 397 Honours Matrix Numerical Analysis (3 credits)

    Offered by: Mathematics and Statistics (Faculty of Science)

    Overview

    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: This course is not scheduled for the 2019-2020 academic year.

    Instructors: There are no professors associated with this course for the 2019-2020 academic year.

At least 8 credits selected from:

  • MATH 523 Generalized Linear Models (4 credits)

    Offered by: Mathematics and Statistics (Faculty of Science)

    Overview

    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. Contingency tables: logistic regression, log-linear models. Censored data. Applications to current problems in medicine, biological and physical sciences. R software.

    Terms: Winter 2020

    Instructors: Neslehova, Johanna (Winter)

    • Winter

    • Prerequisite: MATH 423

    • Restriction: Not open to students who have taken MATH 426

  • MATH 524 Nonparametric Statistics (4 credits)

    Offered by: Mathematics and Statistics (Faculty of Science)

    Overview

    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: This course is not scheduled for the 2019-2020 academic year.

    Instructors: There are no professors associated with this course for the 2019-2020 academic year.

    • Fall

    • Prerequisite: MATH 324 or equivalent

    • Restriction: Not open to students who have taken MATH 424

  • MATH 525 Sampling Theory and Applications (4 credits)

    Offered by: Mathematics and Statistics (Faculty of Science)

    Overview

    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 2019-2020 academic year.

    Instructors: There are no professors associated with this course for the 2019-2020 academic year.

    • Prerequisite: MATH 324 or equivalent

    • Restriction: Not open to students who have taken MATH 425

  • MATH 556 Mathematical Statistics 1 (4 credits)

    Offered by: Mathematics and Statistics (Faculty of Science)

    Overview

    Mathematics & Statistics (Sci) : Distribution theory, stochastic models and multivariate transformations. Families of distributions including location-scale families, exponential families, convolution families, exponential dispersion models and hierarchical models. Concentration inequalities. Characteristic functions. Convergence in probability, almost surely, in Lp and in distribution. Laws of large numbers and Central Limit Theorem. Stochastic simulation.

    Terms: Fall 2019

    Instructors: Stephens, David (Fall)

    • Fall

    • Prerequisite: MATH 357 or equivalent

  • MATH 557 Mathematical Statistics 2 (4 credits)

    Offered by: Mathematics and Statistics (Faculty of Science)

    Overview

    Mathematics & Statistics (Sci) : Sampling theory (including large-sample theory). Likelihood functions and information matrices. Hypothesis testing, estimation theory. Regression and correlation theory.

    Terms: Winter 2020

    Instructors: Asgharian-Dastenaei, Masoud (Winter)

The remaining Mathematics credits selected from:

** MATH 578 and COMP 540 cannot both be taken for program credit.

  • MATH 350 Honours Discrete Mathematics (3 credits)

    Offered by: Mathematics and Statistics (Faculty of Science)

    Overview

    Mathematics & Statistics (Sci) : Discrete mathematics. Graph Theory: matching theory, connectivity, planarity, and colouring; graph minors and extremal graph theory. Combinatorics: combinatorial methods, enumerative and algebraic combinatorics, discrete probability.

    Terms: Fall 2019

    Instructors: Norin, Sergey (Fall)

    • Prerequisites: MATH 235 or MATH 240 and MATH 251 or MATH 223.

    • Restrictions: Not open to students who have taken or are taking MATH 340. Intended for students in mathematics or computer science honours programs.

    • Intended for students in mathematics or computer science honours programs.

  • MATH 352 Problem Seminar (1 credit)

    Offered by: Mathematics and Statistics (Faculty of Science)

    Overview

    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 2019

    Instructors: Norin, Sergey (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.

  • MATH 454 Honours Analysis 3 (3 credits)

    Offered by: Mathematics and Statistics (Faculty of Science)

    Overview

    Mathematics & Statistics (Sci) : Review of point-set topology: topological space, dense sets, completeness, compactness, connectedness and path-connectedness, separability. Arzela-Ascoli, Stone-Weierstrass, Baire category theorems. Measure theory: sigma algebras, Lebesgue measure and integration, L^1 functions. Fatou's lemma, monotone and dominated convergence theorem. Egorov, Lusin's theorems. Fubini-Tonelli theorem.

    Terms: Fall 2019

    Instructors: Vetois, Jerome (Fall)

    • Prerequisite: MATH 255 or equivalent.

    • Restriction: Not open to students who have taken MATH 354.

  • MATH 545 Introduction to Time Series Analysis (4 credits)

    Offered by: Mathematics and Statistics (Faculty of Science)

    Overview

    Mathematics & Statistics (Sci) : Stationary processes; estimation and forecasting of ARMA models; non-stationary and seasonal models; state-space models; financial time series models; multivariate time series models; introduction to spectral analysis; long memory models.

    Terms: Winter 2020

    Instructors: Steele, Russell (Winter)

  • MATH 578 Numerical Analysis 1 (4 credits) **

    Offered by: Mathematics and Statistics (Faculty of Science)

    Overview

    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 2019

    Instructors: Nave, Jean-Christophe (Fall)

  • MATH 587 Advanced Probability Theory 1 (4 credits)

    Offered by: Mathematics and Statistics (Faculty of Science)

    Overview

    Mathematics & Statistics (Sci) : Probability spaces. Random variables and their expectations. Convergence of random variables in Lp. Independence and conditional expectation. Introduction to Martingales. Limit theorems including Kolmogorov's Strong Law of Large Numbers.

    Terms: Fall 2019

    Instructors: Addario-Berry, Dana Louis (Fall)

  • MATH 594 Topics in Mathematics and Statistics (4 credits)

    Offered by: Mathematics and Statistics (Faculty of Science)

    Overview

    Mathematics & Statistics (Sci) : This course covers a topic in mathematics and/or statistics.

    Terms: This course is not scheduled for the 2019-2020 academic year.

    Instructors: There are no professors associated with this course for the 2019-2020 academic year.

    • Prerequisites: At least 30 credits in required or complementary courses from the Honours Mathematics, Honours Applied Mathematics, or Honours Probability and Statistics programs. Additional prerequisites may be imposed by the Department of Mathematics and Statistics depending on the nature of the topic.

    • Restrictions: Requires permission of the Department of Mathematics and Statistics

15 credits in Computer Science selected as follows:

At least 6 credits selected from:

  • COMP 424 Artificial Intelligence (3 credits)

    Offered by: Computer Science (Faculty of Science)

    Overview

    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 2020

    Instructors: Cheung, Jackie; Trischler, Adam (Winter)

  • COMP 462 Computational Biology Methods (3 credits)

    Offered by: Computer Science (Faculty of Science)

    Overview

    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 2019

    Instructors: Blanchette, Mathieu (Fall)

  • COMP 526 Probabilistic Reasoning and AI (3 credits)

    Offered by: Computer Science (Faculty of Science)

    Overview

    Computer Science (Sci) : Belief networks, Utility theory, Markov Decision Processes and Learning Algorithms.

    Terms: This course is not scheduled for the 2019-2020 academic year.

    Instructors: There are no professors associated with this course for the 2019-2020 academic year.

  • COMP 540 Matrix Computations (4 credits) **

    Offered by: Computer Science (Faculty of Science)

    Overview

    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: This course is not scheduled for the 2019-2020 academic year.

    Instructors: There are no professors associated with this course for the 2019-2020 academic year.

  • COMP 547 Cryptography and Data Security (4 credits)

    Offered by: Computer Science (Faculty of Science)

    Overview

    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: Winter 2020

    Instructors: Crepeau, Claude (Winter)

  • COMP 551 Applied Machine Learning (4 credits)

    Offered by: Computer Science (Faculty of Science)

    Overview

    Computer Science (Sci) : Selected topics in machine learning and data mining, including clustering, neural networks, support vector machines, decision trees. Methods include feature selection and dimensionality reduction, error estimation and empirical validation, algorithm design and parallelization, and handling of large data sets. Emphasis on good methods and practices for deployment of real systems.

    Terms: Fall 2019, Winter 2020

    Instructors: Hamilton, William (Fall) Rabbany, Reihaneh; Ravanbakhsh, Mohsen (Winter)

    • Prerequisite(s): MATH 323 or ECSE 205 or ECSE 305 or equivalent

    • Restriction(s): Not open to students who have taken COMP 598 when topic was "Applied Machine Learning"

    • Some background in Artificial Intelligence is recommended, e.g. COMP-424 or ECSE-526, but not required.

  • COMP 552 Combinatorial Optimization (4 credits)

    Offered by: Computer Science (Faculty of Science)

    Overview

    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 2019-2020 academic year.

    Instructors: There are no professors associated with this course for the 2019-2020 academic year.

    • 4 hours

    • Prerequisite: Math 350 or COMP 362 (or equivalent).

    • Restriction: This course is reserved for undergraduate honours students and graduate students. Not open to students who have taken or are taking MATH 552.

  • COMP 564 Advanced Computational Biology Methods and Research (3 credits)

    Offered by: Computer Science (Faculty of Science)

    Overview

    Computer Science (Sci) : Fundamental concepts and techniques in computational structural biology, system biology. Techniques include dynamic programming algorithms for RNA structure analysis, molecular dynamics and machine learning techniques for protein structure prediction, and graphical models for gene regulatory and protein-protein interaction networks analysis. Practical sessions with state-of-the-art software.

    Terms: This course is not scheduled for the 2019-2020 academic year.

    Instructors: There are no professors associated with this course for the 2019-2020 academic year.

  • COMP 566 Discrete Optimization 1 (3 credits)

    Offered by: Computer Science (Faculty of Science)

    Overview

    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: This course is not scheduled for the 2019-2020 academic year.

    Instructors: There are no professors associated with this course for the 2019-2020 academic year.

  • COMP 567 Discrete Optimization 2 (3 credits)

    Offered by: Computer Science (Faculty of Science)

    Overview

    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: Winter 2020

    Instructors: Ferland, Jacques; Dimitrakopoulos, Roussos G (Winter)

The remaining Computer Science credits are selected from COMP courses at the 300 level or above excluding COMP 396.

Faculty of Science—2019-2020 (last updated Aug. 20, 2019) (disclaimer)
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