Tous les cours ne sont pas nécessairement offerts chaque année. Les cours sont offerts dans la langue dans laquelle ils sont décrits.
Les cotes de cours entre parenthèses sont celles de la Carleton University. Un cours de 3 crédits à l’Université d’Ottawa correspond à un cours de 0,5 crédit à la Carleton University.
Not all of the listed courses are given each year. The course is offered in the language in which it is described.
Course codes in parentheses are for Carleton University. A 3-credit course at the University of Ottawa is equivalent to a 0.5-credit course at Carleton University.
BCH8102 SELECTED TOPICS IN PROTEIN STRUCTURE AND FUNCTION (3cr.)
An advanced study of recent literature dealing with structure-function relationships in selected proteins.
BCH8108 ADVANCED METHODS OF MACROMOLECULAR STRUCTURE DETERMINATION (3cr.)
A detailed examination of modern methods used to determine the structures of proteins, nucleic acids, and carbohydrates. May include X-ray crystallography, electron diffraction, nuclear magnetic resonance, and other spectroscopic methods.
BIO5207 (BIOL 5500) SELECTED TOPICS (6cr.)
Courses in selected aspects of specialized biological subjects, not covered by other graduate courses; course details will be available at registration.
BIO5302 (BIOL 5105) METHODS IN MOLECULAR GENETICS (3cr.)
Theory and associated applications of emerging methods in molecular genetics, including information gathered from large-scale genome-wide analysis and protein-protein interaction data, and how this information can advance understanding of cell biology.
BIO5306 (BIOL 5409) MODELLING FOR BIOLOGISTS (3cr.)
Use and limitations of mathematical and simulation modelling approaches for the study of biological phenomena.
BIO8100 (BIOL 5501) SELECTED TOPICS IN BIOLOGY I (3cr.)
Lectures and/or seminars dealing with current advances in a selected area or branch of biology, not covered by other graduate courses.
BIO8102 (BIOL 5502) SPECIAL TOPICS IN BIOLOGY (3cr.)
Selected aspects of specialized biological subjects not covered by other graduate courses.
BIO8301 (BIOL 5201) EVOLUTIONARY BIOINFORMATICS (3cr.)
Fundamental concepts in molecular evolution and hands-on experience with computer analysis of DNA sequences. Topics may include molecular sequence databases, multiple alignments and phylogenetic trees.
BNF5106 BIOINFORMATICS (3cr.)
Major concepts and methods of bioinformatics. Topics may include, but are not limited to: genetics, statistics & probability theory, alignments, phylogenetics, genomics, data mining, protein structure, cell simulation and computing.
BNF5107 APPLIED BIOINFORMATICS (3cr.)
Computational knowledge discovery in and the dynamic nature of cellular networks. Includes, but is not limited to, knowledge representation, large scale data integration, data mining and computational systems biology.
BNF5506 BIOINFORMATIQUE (3cr.)
Concepts et méthodes en bioinformatique. Les sujets abordés peuvent inclure, entre autres, la génétique, les statistiques et les théories des probabilités, les alignements, la phylogénétique, la génomique et la structure de protéines.
BNF6100 MSc SEMINAR (3cr.)
Current topics in bioinformatics presented by program professors and invited speakers. Oral presentation and written report required. Graded S/NS.
BNF6500 SÉMINAIRE DE MAÎTRISE (3cr.)
Sujets courants en bioinformatique présentés par des professeurs membres du programme et des conférenciers invités. Présentation orale et rapport écrit requis. Noté S/NS.
CMM5111 COMPUTATIONAL CELL BIOLOGY (3cr.)
Emphasis is on providing students with the background knowledge and the tools needed to develop and analyze models of cellular processes. Topics include modelling enzyme kinetics, signal transduction pathways, and gene regulatory networks, using differential equations, nonlinear dynamics, and stochastic processes. Prerequisite: permission of program director and course coordinator.
CMM5304 INTRODUCTION TO DEVELOPMENTAL BIOLOGY (3cr.)
Concepts in development and signalling pathways during development including formation of the germ layers; establishment of the body axis and principles of segmentation; patterning and homeobox genes; neurogenesis; axonal and neuronal guidance; stem cell concepts; germ cells; animal models in developmental biology.
CMM8310 CURRENT TOPICS IN RNA MOLECULAR BIOLOGY (3cr.)
Properties, mechanisms associated with regulation and the function of RNAs and Ribonucleoprotein (RNPs) as well as RNA organisms. Current knowledge on RNA expression (synthesis, processing, transport and localization), the structure-function relationship and molecular mechanisms associated with RNAs and RNA genomes, RNA in evolution and in the origin of life, and RNA as therapeutic agents. Prerequisites: BCH/BIO 3570-3170 or equivalent with the permission of the program director. Exclusion: BCH 8310.
CSI5100 (COMP 5306) DATA INTEGRATION (3cr.)
Materialized and virtual approaches to integration of heterogeneous and independent data sources. Emphasis on data models, architectures, logic-based techniques for query processing, metadata and consistency management, the role of XML and ontologies in data integration; connections to schema mapping, data exchange, and P2P systems.
Prerequisite: COMP 3005 or equivalent.
CSI5101 (COMP 5307) KNOWLEDGE REPRESENTATION (3cr.)
KR is concerned with representing knowledge and using it in computers. Emphasis on logic-based languages for KR, and automated reasoning techniques and systems; important applications of this traditional area of AI to ontologies and semantic web.
Prerequisites: COMP 1805 and COMP 3005, or equivalents.
CSI5126 (COMP 5108) ALGORITHMS IN BIOINFORMATICS (3cr.)
Fundamental mathematical and algorithmic concepts underlying computational molecular biology; physical and genetic mapping, sequence analysis (including alignment and probabilistic models), genomic rearrangement, phylogenetic inference, computational proteomics and systemics modelling of the whole cell. Prerequisites: CSI 3105, COMP 3804 or equivalent.
CSI5131 (COMP 5704) PARALLEL ALGORITHMS AND THEIR IMPLEMENTATION (3cr.)
Introduction: models of computation, levels of parallelism; performance measures for parallel algorithms;
need for parallel algorithms. Parallel algorithms: techniques in matrix multiplication, solution of linear
equations, transforms and differential equations; systolic arrays for the implementation of parallel algorithms in the areas of matrix arithmetic, transforms and relational database operations. VLSI implementations: VLSI and parallel computing structures; mapping of high-level computations into VLSI structures.
CSI5132 (COMP 5105) PARALLEL PROCESSING SYSTEMS (3cr.)
Introduction to issues involved in designing and using parallel processing systems. Topics include: taxonomy and applications of parallel systems; SIMD systems; multiprocessor systems; multicomputer systems; computation versus communication issues in parallel processing; scheduling parallel systems; spinning versus blocking; interconnection networks; hot-spot contention.
Prerequisite: permission of the School.
CSI5163 (COMP 5703) ALGORITHM ANALYSIS AND DESIGN (3cr.)
Topics of current interest in the design and analysis of computer algorithms for graph-theoretical applications; e.g. shortest paths, chromatic number, etc. Lower bounds, upper bounds, and average performance of algorithms. Complexity theory.
CSI5165 (COMP 5709) COMBINATORIAL ALGORITHMS (3cr.)
Design of algorithms for solving problems that are combinatorial in nature, using both sequential and
parallel models of computation. Parallel algorithms for enumerating basic combinatorial objects (permutations, combinations, set partitions) and for solving optimization problems (knapsack, minimal cover, branch-and-bound).
Polyminoes, polygonal systems, enumeration and classification and benzenoid and coronoid hydrocarbons
in chemistry. Combinatorial geometry (Voronoi diagrams, polytopes arrangements). Algorithmic problems
in many-valued logics (base enumeration, tautology
checking, minimization, finding the spectra).
CSI5387 (COMP 5706) DATA MINING AND CONCEPT LEARNING (3cr.)
Data mining as finding associations, clustering, and concept learning. Basic issues of associations and selected concept representations. Introduction to data warehousing. Concept learning viewed as a search
problem. Standard concept induction algorithms. The use of neural networks for representing and learning
concepts. Knowledge-intensive concept learning. Introduction to the formal theory of concept learnability.
Instance-based learning. Selected applications of data mining and concept learning. Prerequisite: CSI 4106 or permission of the program director.
CSI5526 (COMP 5180) ALGORITHMES EN BIOINFORMATIQUE (3cr.)
Assemblage de l'ADN, recherche de gênes, comparaison de chaînes, alignement de séquences, structures grammaticales, structures secondaires et tertiaires. Les récents développements, tels que les puces d'ADN et de protéines. Travail additionnel requis dans le cas des étudiants inscrits sous la cote CSI 5526. Préalable: CSI
3505 ou (dans le cas des étudiants diplômés) permission
du responsable de programme.
CSI5565 (COMP 5709) ALGORITHMES COMBINATOIRES (3cr.)
Conception d'algorithmes de problèmes de nature combinatoire, à l'aide de modèles séquentiels et parallèles. Algorithmes parallèles pour l'énumération d'objets combinatoires de base (permutations, combinaisons, partitions), et pour résoudre des problèmes d'optimisation (knapsack, recouvrement minimal, méthode branch-and-bound); systèmes polygonaux, applications en chimie; géométrie combinatoire (diagrammes de Voronoi, polytopes, arrangements); problèmes en logique à valeur multiple, énumération de base, vérification de tautologie, minimisation, recherche du spectre.
MAT5170 (STAT 5708) PROBABILITY THEORY I (3cr.)
Probability spaces, random variables, expected values as integrals, joint distributions, independence and product measures, cumulative distribution functions and extensions of probability measures, Borel-Cantelli lemmas, convergence concepts, independent identically distributed sequences of random variables. Prerequisites: Permission of Program Director.
MAT5171 (MATH 5709) PROBABILITY THEORY II (3cr.)
Laws of large numbers, characteristic functions, central limit theorem, conditional probabilities and expectation, basic properties and convergence theorems for martingales, introduction to Brownian motion. Prerequisite: MAT 5170 (STAT 5708).
MAT5181 (STAT 5703) DATA MINING I (3cr.)
Visualization and knowledge discovery in massive datasets; unsupervised learning: clustering algorithms; dimension reduction; supervised learning: pattern recognition, smoothing techniques, classification. Computer software will be used. Prerequisite: Permission of the Instructor.
MAT5182 (STAT 5702) MODERN APPLIED / COMPUTATIONAL STATISTICS (3cr.)
Resampling and computer intensive methods: bootstrap, jackknife with applications to bias estimation, variance estimation, confidence intervals, and regression analysis. Smoothing methods in curve estimation; Statistical classification and pattern recognition: error counting methods, optimal classifiers, bootstrap estimates of the bias of the misclassification error.
MAT5190 (STAT 5600) MATHEMATICAL STATISTICS I (3cr.)
Statistical decision theory; likelihood functions; sufficiency; factorization theorem; exponential families; UMVU estimators; Fisher's information; Cramer-Rao lower bound; maximum likelihood and moment estimation; invariant and robust point estimation; asymptotic properties; Bayesian point estimation. Prerequisites: MAT 3172 and MAT 3375.
MAT5191 (STAT 5501) MATHEMATICAL STATISTICS II (3cr.)
Confidence intervals and pivotals; Bayesian intervals; optimal tests and Neyman-Pearson theory; likelihood ratio and score tests; significance tests; goodness-of-fit tests; large sample theory and applications to maximum likelihood and robust estimation. Prerequisite: MAT 5190.
MAT5198 (MATH 5701) STOCHASTIC MODELS (3cr.)
Markov systems, stochastic networks, queuing networks, spatial processes, approximation methods in stochastic processes and queuing theory. Applications to the modelling and analysis of computer-communications systems and other distributed networks.
MAT5314 (MATH 6508) TOPICS IN PROBABILITY AND STATISTICS (3cr.)
MAT5319 (MATH 6507) TOPICS IN PROBABILITY AND STATISTICS (3cr.)
MAT5570 (STAT 5708) THÉORIE DES PROBABILITÉS I (3cr.)
Espaces probabilisés, variables aléatoires, l'espérance mathématique définie comme une intégrale, lois conjointes, indépendance et mesure produit, répartitions et extensions de mesures de probabilité, lemmes de Borel-Cantelli, notions de convergence, suites de variables aléatoires indépendantes et équidistribuées. Préalables : MAT 3525 et MAT 3572 (MATH 3001, MATH 3002 et MATH 3500).
MAT5571 (STAT 5709) THÉORIE DES PROBABILITÉS II (3cr.)
Lois des grands nombres, fonctions caractéristiques, théorème-limite central, probabilité et espérance conditionnelles, propriétés élémentaires et théorèmes de convergence des martingales, introduction au mouvement brownien. Préalable : MAT 5570 (STAT 5708).
MAT5591 (STAT 5501) INFÉRENCE STATISTIQUE (3cr.)
MAT5598 (MATH 5701) MODÈLES STOCHASTIQUES (3cr.)
SYS5120 APPLIED PROBABILITY (3cr.)
An introduction to stochastic processes, with emphasis on regenerative phenomena. Review of limit theorems and conditioning. The Poisson process. Renewal theory and limit theorems for regenerative processes; Discrete-time and continuous-time Markov processes with countable state space. Applications to queueing.
ELG6114 (SYSC 5104) METHODOLOGIES FOR DISCRETE-EVENT MODELLING AND SIMULATION (3cr.)
Methodological aspects of simulation. Modelling discrete events systems. Modelling formalisms: FSA, FSM, Petri Nets, DEVS, others. Verification and validation. Cellular models: cellular automata, cell-DEVS. Continuous and
hybrid models. Parallel and distributed simulation (PADS) techniques. PADS middleware: HLA, parallel-DEVS, Time-warp. Prerequisites: knowledge of C++ and of basic concepts of concurrency and distributed systems.