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-unit course at the University of Ottawa is equivalent to a 0.5-unit course at Carleton University.
BCH8102 SELECTED TOPICS IN PROTEIN STRUCTURE AND FUNCTION (3 units)
An advanced study of recent literature dealing with structure-function relationships in selected proteins.
BCH8108 ADVANCED METHODS OF MACROMOLECULAR STRUCTURE DETERMINATION (3 units)
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 (6 units)
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 (3 units)
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. Prerequisites: Graduate standing and permission of the department.
BIO5306 (BIOL 5409) MODELLING FOR BIOLOGISTS (3 units)
Use and limitations of mathematical and simulation modelling approaches for the study of biological phenomena.
BIO8100 (BIOL 5501) SELECTED TOPICS IN BIOLOGY I (3 units)
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 (3 units)
Selected aspects of specialized biological subjects not covered by other graduate courses.
BIO8301 (BIOL 5201) EVOLUTIONARY BIOINFORMATICS (3 units)
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. Prerequisite: Graduate standing plus basic courses in genetics and evolution; permission of the department.
BMG5111 (BIOM 5403) ADVANCED TOPICS IN MEDICAL INFORMATICS AND TELEMEDICINE (3 units)
Recent and advanced topics in the field of medical informatics and telemedicine and its related areas. Prerequisite: permission of the program director.
BMG5317 (BIOM 5400) MEDICAL COMPUTING (3 units)
Introduction to the information technology research used in medically related fields such as biotechnology, cancer treatment, and biometrics. Topics of current interest such as medical imaging, telemedicine, telesurgery, DNA analysis, and medical information systems.
BNF5106 BIOINFORMATICS (3 units)
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 (3 units)
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.
BNF6100 MSc SEMINAR (3 units)
Current topics in bioinformatics presented by program professors and invited speakers. Oral presentation and written report required. Graded S/NS.
CMM5111 COMPUTATIONAL CELL BIOLOGY (3 units)
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 (3 units)
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 (3 units)
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 (3 units)
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 (3 units)
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 (3 units)
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. Prerequisite: CSI3105 or (in case of graduate students) permission of the instructor.
CSI5131 (COMP 5704) PARALLEL ALGORITHMS AND APPLICATIONS IN BIOINFORMATICS (3 units)
Multiprocessor architectures from an application programmer's perspective: programming models, processor clusters, multi-core processors, GPUs, algorithmic paradigms, efficient parallel problem solving, scalability and portability. Projects on high performance computing in Data Science, incl. data analytics, bioinformatics, simulations. Programming experience on parallel processing equipment. Prerequisite: COMP 3804 or equivalent.
CSI5163 (COMP 5703) ALGORITHM ANALYSIS AND DESIGN (3 units)
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 (3 units)
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 (3 units)
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.
ELG6114 (SYSC 5104) METHODOLOGIES FOR DISCRETE-EVENT MODELLING AND SIMULATION (3 units)
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.
ELG6173 (SYSC 5703) INTEGRATED DATABASE SYSTEMS
Database definitions, applications, and architectures. Conceptual design based on the entity-relationship
and object-oriented models. Relational data model: relational algebra and calculus, normal forms, data
definition and manipulation languages. Database management systems: transaction management,
recovery and concurrency control. Current trends: object-oriented, knowledge-based, multimedia and
distributed databases. Prerequisite: SYSC 5704 (ELG 6174) or the equivalent.
MAT5170 (STAT 5708) PROBABILITY THEORY I (3 units)
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. Prerequisites: MAT3125 and MAT3172 (MATH 3001, MATH 3002 and MATH 3500).
MAT5171 (MATH 5709) PROBABILITY THEORY II (3 units)
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 (3 units)
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 (3 units)
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 (3 units)
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. Prerequisites: MAT3172 and MAT3375.
MAT5191 (STAT 5501) MATHEMATICAL STATISTICS II (3 units)
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 (3 units)
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 (3 units)
MAT5319 (MATH 6507) TOPICS IN PROBABILITY AND STATISTICS (3 units)
SYS5120 APPLIED PROBABILITY (3 units)
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. Prerequisites: MAT2341 and MAT2371 and MAT2375.