# Major Statistics and Computer Science (72 credits)

Offered by: Mathematics and Statistics     Degree: Bachelor of Science

### Program Requirements

This program provides students with a solid training in both computer science and statistics together with the necessary mathematical background. As statistical endeavours involve ever increasing amounts of data, some students may want training in both disciplines.

#### Program Prerequisites

Students entering the Joint Major 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 72 credits of required courses.

• 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 2018, Winter 2019, Summer 2019

Instructors: Jerome Fortier, Liangming Shen, Yann Batiste Pequignot, Damian Osajda (Fall) Jerome Fortier (Winter) Rebecca Patrias (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 2018, Winter 2019, Summer 2019

Instructors: Sidney Trudeau, Jerome Fortier, Rebecca Patrias (Fall) Alexander Garver (Winter) Peter Zenz (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 2018, Winter 2019, Summer 2019

Instructors: Corentin Perret-Gentil-dit-Maillard, Jonah Gaster (Fall) Sidney Trudeau, Jerome Fortier, Thomas F Fox (Winter) Bogdan Nica (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 (51 credits)

* Students who have sufficient knowledge in a programming language do not need to take COMP 202 but can replace it with an additional Computer Science complementary course.

** Students take either COMP 350 or MATH 317, but not both.

*** Students take either MATH 223 or MATH 236, but not both.
Both courses are equivalent as prerequisites for required and complementary Computer Science courses listed below.

• 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 2018, Winter 2019, Summer 2019

Instructors: Giulia Alberini, Joseph P Vybihal (Fall) Giulia Alberini, Tzu-Yang Yu (Winter) Tzu-Yang Yu (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 2018, Winter 2019

Instructors: David Meger (Fall) Chad Zammar, Joseph P Vybihal (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 2018, Winter 2019

Instructors: Michael Langer, Giulia Alberini (Fall) Martin Robillard, Giulia Alberini (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 251 Algorithms and Data Structures (3 credits)

Offered by: Computer Science (Faculty of Science)

### Overview

Computer Science (Sci) : Introduction to algorithm design and analysis. Graph algorithms, greedy algorithms, data structures, dynamic programming, maximum flows.

Terms: Fall 2018, Winter 2019

Instructors: Jérôme Waldispuhl (Fall) Luc P Devroye, Erin Leigh McLeish (Winter)

• 3 hours

• Prerequisite: COMP 250

• Corequisite(s): MATH 235 or MATH 240 or MATH 363.

• COMP 251 uses mathematical proof techniques that are taught in the corequisite course(s). If possible, students should take the corequisite course prior to COMP 251.

• COMP 251 uses basic counting techniques (permutations and combinations) that are covered in MATH 240 and 363, but not in MATH 235. These techniques will be reviewed for the benefit of MATH 235 students.

• Restrictions: Not open to students who have taken or are taking COMP 252.

• 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 2018, Winter 2019

Instructors: Paul Kry (Fall) Kaleem Siddiqi (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 2018, Winter 2019

Instructors: Brigitte Pientka (Fall) Prakash Panangaden (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 2018

• COMP 350 Numerical Computing (3 credits) **

Offered by: Computer Science (Faculty of Science)

### Overview

Computer Science (Sci) : Computer representation of numbers, IEEE Standard for Floating Point Representation, computer arithmetic and rounding errors. Numerical stability. Matrix computations and software systems. Polynomial interpolation. Least-squares approximation. Iterative methods for solving a nonlinear equation. Discretization methods for integration and differential equations.

Terms: Fall 2018

Instructors: Xiao-Wen Chang (Fall)

• COMP 360 Algorithm Design (3 credits)

Offered by: Computer Science (Faculty of Science)

### Overview

Computer Science (Sci) : Advanced algorithm design and analysis. Linear programming, complexity and NP-completeness, advanced algorithmic techniques.

Terms: Fall 2018, Winter 2019

Instructors: Bruce Alan Reed (Fall) Bruce Alan Reed (Winter)

• MATH 222 Calculus 3 (3 credits)

Offered by: Mathematics and Statistics (Faculty of Science)

### Overview

Mathematics & Statistics (Sci) : Taylor series, Taylor's theorem in one and several variables. Review of vector geometry. Partial differentiation, directional derivative. Extreme of functions of 2 or 3 variables. Parametric curves and arc length. Polar and spherical coordinates. Multiple integrals.

Terms: Fall 2018, Winter 2019, Summer 2019

Instructors: Jeremy Macdonald, Dmitry Faifman (Fall) Lars Sektnan (Winter) Yann Batiste Pequignot (Summer)

• MATH 223 Linear Algebra (3 credits) ***

Offered by: Mathematics and Statistics (Faculty of Science)

### Overview

Mathematics & Statistics (Sci) : Review of matrix algebra, determinants and systems of linear equations. Vector spaces, linear operators and their matrix representations, orthogonality. Eigenvalues and eigenvectors, diagonalization of Hermitian matrices. Applications.

Terms: Fall 2018, Winter 2019

Instructors: Djivede Kelome (Fall) Jeremy Macdonald (Winter)

• Fall and Winter

• Prerequisite: MATH 133 or equivalent

• Restriction: Not open to students in Mathematics programs nor to students who have taken or are taking MATH 236, MATH 247 or MATH 251. It is open to students in Faculty Programs

• 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 2018

Instructors: Daniel Wise (Fall)

• Fall

• 3 hours lecture; 1 hour tutorial

• Prerequisite: MATH 133 or equivalent

• MATH 236 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 mappings. Matrix representation of linear mappings. Determinants. Eigenvectors and eigenvalues. Diagonalizable operators. Cayley-Hamilton theorem. Bilinear and quadratic forms. Inner product spaces, orthogonal diagonalization of symmetric matrices. Canonical forms.

Terms: Winter 2019

Instructors: Bogdan Nica (Winter)

• 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 2018

Instructors: Jerome Vetois (Fall)

• Fall

• Prerequisite: MATH 141

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

• MATH 314 Advanced Calculus (3 credits)

Offered by: Mathematics and Statistics (Faculty of Science)

### Overview

Mathematics & Statistics (Sci) : Derivative as a matrix. Chain rule. Implicit functions. Constrained maxima and minima. Jacobians. Multiple integration. Line and surface integrals. Theorems of Green, Stokes and Gauss. Fourier series with applications.

Terms: Fall 2018, Winter 2019

Instructors: Charles Roth (Fall) Stephen W Drury (Winter)

• MATH 317 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: Fall 2018

Instructors: Peter Bartello (Fall)

• MATH 323 Probability (3 credits)

Offered by: Mathematics and Statistics (Faculty of Science)

### Overview

Mathematics & Statistics (Sci) : Sample space, events, conditional probability, independence of events, Bayes' Theorem. Basic combinatorial probability, random variables, discrete and continuous univariate and multivariate distributions. Independence of random variables. Inequalities, weak law of large numbers, central limit theorem.

Terms: Fall 2018, Winter 2019, Summer 2019

Instructors: David Stephens (Fall) David B Wolfson (Winter) Djivede Kelome (Summer)

• Prerequisites: MATH 141 or equivalent.

• Restriction: Intended for students in Science, Engineering and related disciplines, who have had differential and integral calculus

• Restriction: Not open to students who have taken or are taking MATH 356

• MATH 324 Statistics (3 credits)

Offered by: Mathematics and Statistics (Faculty of Science)

### Overview

Mathematics & Statistics (Sci) : Sampling distributions, point and interval estimation, hypothesis testing, analysis of variance, contingency tables, nonparametric inference, regression, Bayesian inference.

Terms: Fall 2018, Winter 2019

Instructors: Abbas Khalili Mahmoudabadi (Fall) Masoud Asgharian-Dastenaei (Winter)

• Fall and Winter

• Prerequisite: MATH 323 or equivalent

• Restriction: Not open to students who have taken or are taking MATH 357

• You may not be able to receive credit for this course and other statistic courses. Be sure to check the Course Overlap section under Faculty Degree Requirements in the Arts or Science section of the Calendar.

• MATH 423 Regression and Analysis of Variance (3 credits)

Offered by: Mathematics and Statistics (Faculty of Science)

### Overview

Mathematics & Statistics (Sci) : Least-squares estimators and their properties. Analysis of variance. Linear models with general covariance. Multivariate normal and chi-squared distributions; quadratic forms. General linear hypothesis: F-test and t-test. Prediction and confidence intervals. Transformations and residual plot. Balanced designs.

Terms: Fall 2018

Instructors: Yi Yang (Fall)

#### Complementary Courses (21 credits)

Revision, April 2018. Start of revision.

12 credits in Mathematics selected from:

* Students take either MATH 340 or MATH 350, but not both.
** MATH 578 and COMP 540 cannot both be taken for program credit.

• MATH 327 Matrix Numerical Analysis (3 credits)

Offered by: Mathematics and Statistics (Faculty of Science)

### Overview

Mathematics & Statistics (Sci) : An overview of numerical methods for linear algebra applications and their analysis. Problem classes include linear systems, least squares problems and eigenvalue problems.

Terms: Winter 2019

Instructors: Ivo Panayotov (Winter)

• MATH 340 Discrete Structures 2 (3 credits) *

Offered by: Mathematics and Statistics (Faculty of Science)

### Overview

Mathematics & Statistics (Sci) : Review of mathematical writing, proof techniques, graph theory and counting. Mathematical logic. Graph connectivity, planar graphs and colouring. Probability and graphs. Introductory group theory, isomorphisms and automorphisms of graphs. Enumeration and listing.

Terms: Winter 2019

Instructors: Jerome Fortier (Winter)

• MATH 350 Graph Theory and Combinatorics (3 credits) *

Offered by: Mathematics and Statistics (Faculty of Science)

### Overview

Mathematics & Statistics (Sci) : Graph models. Graph connectivity, planarity and colouring. Extremal graph theory. Matroids. Enumerative combinatorics and listing.

Terms: Fall 2018

• 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 2018

Instructors: Jacques Claude Hurtubise (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 410 Majors Project (3 credits)

Offered by: Mathematics and Statistics (Faculty of Science)

### Overview

Mathematics & Statistics (Sci) : A supervised project.

Terms: Fall 2018, Winter 2019, Summer 2019

Instructors: Djivede Kelome, Johanna Neslehova, Jean-Philippe Lessard, David Stephens, Russell Steele, Gantumur Tsogtgerel (Fall) Djivede Kelome, David Stephens, Dmitry Jakobson, Christian Genest, Russell Steele, Gantumur Tsogtgerel, Masoud Asgharian-Dastenaei, Niky Kamran (Winter) Djivede Kelome, Russell Steele (Summer)

• Prerequisite: Students must have 21 completed credits of the required mathematics courses in their program, including all required 200 level mathematics courses.

• Requires departmental approval.

• MATH 427 Statistical Quality Control (3 credits)

Offered by: Mathematics and Statistics (Faculty of Science)

### Overview

Mathematics & Statistics (Sci) : Introduction to quality management; variability and productivity. Quality measurement: capability analysis, gauge capability studies. Process control: control charts for variables and attributes. Process improvement: factorial designs, fractional replications, response surface methodology, Taguchi methods. Acceptance sampling: operating characteristic curves; single, multiple and sequential acceptance sampling plans for variables and attributes.

Terms: This course is not scheduled for the 2018-2019 academic year.

Instructors: There are no professors associated with this course for the 2018-2019 academic year.

• MATH 447 Introduction to Stochastic Processes (3 credits)

Offered by: Mathematics and Statistics (Faculty of Science)

### Overview

Mathematics & Statistics (Sci) : Conditional probability and conditional expectation, generating functions. Branching processes and random walk. Markov chains, transition matrices, classification of states, ergodic theorem, examples. Birth and death processes, queueing theory.

Terms: Winter 2019

Instructors: Russell Steele (Winter)

• Winter

• Prerequisite: MATH 323

• Restriction: Not open to students who have taken or are taking MATH 547.

• 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 2019

Instructors: Johanna Neslehova (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: Fall 2018

Instructors: Christian Genest (Fall)

• 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: Winter 2019

Instructors: Russell Steele (Winter)

• Prerequisite: MATH 324 or equivalent

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

• 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: Fall 2018

Instructors: Russell Steele (Fall)

• 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 2018

• 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: Winter 2019

Instructors: Michael Pichot, Djivede Kelome, Johanna Neslehova (Winter)

• 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

Revision, April 2018. End of revision.

9 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 2019

Instructors: Jackie Cheung (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 2018

Instructors: Mathieu Blanchette (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 2018-2019 academic year.

Instructors: There are no professors associated with this course for the 2018-2019 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: Fall 2018

Instructors: Xiao-Wen Chang (Fall)

• 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: This course is not scheduled for the 2018-2019 academic year.

Instructors: There are no professors associated with this course for the 2018-2019 academic year.

• 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 2018, Winter 2019

Instructors: Sarath Chandar (Fall) William Hamilton (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 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: Winter 2019

Instructors: Jérôme Waldispuhl (Winter)

• 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 2018-2019 academic year.

Instructors: There are no professors associated with this course for the 2018-2019 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 2019

Instructors: Roussos G Dimitrakopoulos, Jacques Ferland (Winter)

The remaining Computer Science credits are selected from COMP courses at the 300 level or above (except COMP 396) and ECSE 508.

Faculty of Science—2018-2019 (last updated Aug. 22, 2018) (disclaimer)