School of Computer Science
Herzberg Bldg 5302
613-520-4333
http://scs.carleton.ca
- M.C.S. Computer Science
- M.C.S. Computer Science, Accelerated Stream
- M.C.S. Computer Science with Specialization in Bioinformatics
- M.C.S. Computer Science with Specialization in Data Science
- Ph.D. Computer Science
M.C.S. Computer Science
About the Program
The M.C.S. program leads to a graduate degree in Computer Science. The program combines breadth knowledge in computer science through course work and depth knowledge in the area of specialization through research (M.C.S. Thesis option) or through course work and a project (M.C.S. non-Thesis option).
The program emphasizes problems of current practical significance and advances in computer science knowledge. The program fosters collaborative and interdisciplinary work with the scientific and industrial (private and public) communities.
Academic Regulations
See the General Regulations section of this Calendar.
Admission Requirements for M.C.S. and M.C.S. Bioinformatics
See the General Regulations section of this Calendar for detailed admission requirements. Applicants should have an Honours bachelor's degree in computer science or the equivalent. By equivalent is meant an Honours degree in a program that includes at least twelve computer science half-credits, two of which must be at the 4000-level, and eight half- credits in mathematics, one of which must be at the 3000- or 4000-level.
Applicants who have a general (3-year) bachelor's degree, or who otherwise lack the required undergraduate preparation, may be admitted to a qualifying-year program. Refer to the General Regulations section of this Calendar for regulations governing the qualifying year.
Admission Requirements for M.C.S. Accelerated Stream
Applicants must have a Carleton B.C.S. Honours degree with at least two COMP courses at the 5000-level with a grade of B+ or higher. See the General Regulations section of this Calendar for detailed admission requirements.
Program Requirements
Normally, students in the program will be expected to complete a thesis; however, students who have substantial relevant work experience may be permitted to take the non-thesis option, which must include a graduate research project course. Students in the Accelerated Stream must complete a thesis.
| M.C.S. Computer Science - Thesis option (5.0 credits) (except for accelerated stream) | ||
| 1. 2.5 credits in course work | 2.5 | |
| 2. Fulfilment of the graduate seminar requirement | ||
| 3. 2.5 credits in: | 2.5 | |
COMP 5905 [2.5] | M.C.S. Thesis (Each candidate submitting a thesis will be required to undertake an oral defence of the thesis.) | |
| Total Credits | 5.0 | |
| M.C.S. Computer Science - Accelerated stream (4.0 credits) | ||
| 1. 1.5 credits in course work | 1.5 | |
| 2. Fulfilment of the graduate seminar requirement | ||
| 3. 2.5 credits in: | 2.5 | |
COMP 5905 [2.5] | M.C.S. Thesis (Each candidate submitting a thesis will be required to undertake an oral defence of the thesis.) | |
| Total Credits | 4.0 | |
| M.C.S. Computer Science - Non-thesis option (5.0 credits) | ||
| 1. 4.0 credits in course work | 4.0 | |
| 2. Fulfilment of the graduate seminar requirement | ||
| 3. 1.0 credit in graduate project (a project may be completed in one or two terms) | 1.0 | |
| Total Credits | 5.0 | |
Notes:
- Course selections must be approved by the student's academic adviser, and must include at least (see OCICS course listing): 0.5 credit in software engineering, 0.5 credit in the theory of computing, and 0.5 credit in either computer applications or computer systems. For students in the Accelerated Stream, the 5000-level COMP courses taken as part of the B.C.S. degree can be used to satisfy area requirements.
- The graduate seminar requirement includes a seminar presentation and participation in at least ten sessions in the joint graduate student seminar series.
- Both course and thesis work may be completed either by full-time or part-time study.
- A candidate can be permitted to carry out thesis work off campus provided suitable arrangements are made for supervision and experimental work, and prior approval is given by the Institute.
| M.C.S. Computer Science with Specialization in Bioinformatics - Thesis Option (5.5 credits) | ||
| 1. 1.0 credit in: | 1.0 | |
BIOL 5515 [0.5] | Bioinformatics | |
BIOL 5517 [0.5] | Bioinformatics Seminar | |
| 2. 2.0 additional credits in course work | 2.0 | |
| 3. Fulfullment of the graduate seminar requirement | ||
| 3. 2.5 credits in: | 2.5 | |
COMP 5905 [2.5] | M.C.S. Thesis (Each candidate submitting a thesis will be required to undertake an oral defence of the thesis.) | |
| Total Credits | 5.5 | |
Bioinfomatics Related Courses
BIOL 5105 (BIO 5302) | Methods in Molecular Genetics | |
BIOL 5201 (BIO 8301) | Evolutionary Bioinformatics | |
BIOL 5409 (BIO 5306) | Modelling for Biologists | |
BIOL 5501 (BIO 8100) | Directed Studies in Biology | |
BIOL 5502 (BIO 8102) | Selected Topics in Biology | |
BIOL 5516 (BNF 5107) | Applied Bioinformatics | |
COMP 5306 (CSI 5100) | Data Integration | |
COMP 5307 (CSI 5101) | Knowledge Representation | |
COMP 5704 (CSI 5131) | Parallel Algorithms and Applications in Data Science | |
COMP 5703 (CSI 5163) | Algorithm Analysis and Design | |
COMP 5108 (CSI 5126) | Algorithms in Bioinformatics | |
STAT 5708 (MAT 5170) | Probability Theory I | |
STAT 5709 (MAT 5171) | Probability Theory II | |
STAT 5703 (MAT 5181) | Data Mining | |
STAT 5702 (MAT 5182) | Modern Applied and Computational Statistics | |
STAT 5600 (MAT 5190) | Mathematical Statistics I | |
STAT 5501 (MAT 5191) | Mathematical Statistics II | |
MATH 6508 (MAT 5314) | Topics in Probability and Stats | |
MATH 6507 (MAT 5319) | Topics in Probability and Stats | |
SYSC 5104 (ELG 6114) | Methodologies For Discrete-Event Modeling And Simulation |
| M.C.S. Computer Science with Specialization in Data Science - Thesis Option (5.0 credits) | ||
| 1. 0.5 credit in: | 0.5 | |
DATA 5000 [0.5] | Data Science Seminar | |
| 2. 1.0 credit from: | 1.0 | |
COMP 5100 [0.5] | Topics in Artificial Intelligence | |
COMP 5101 [0.5] | Distributed Databases and Transaction Processing Systems | |
COMP 5107 [0.5] | Statistical and Syntactic Pattern Recognition | |
COMP 5108 [0.5] | Algorithms in Bioinformatics | |
COMP 5111 [0.5] | Data Management for Business Intelligence | |
COMP 5112 [0.5] | Algorithms for Data Science | |
COMP 5204 [0.5] | Computational Aspects of Geographic Information Systems | |
COMP 5209 [0.5] | Visual Analytics | |
COMP 5305 [0.5] | Advanced Database Systems | |
COMP 5306 [0.5] | Data Integration | |
COMP 5307 [0.5] | Knowledge Representation | |
COMP 5308 [0.5] | Topics in Medical Computing | |
COMP 5401 [0.5] | Electronic Commerce Technologies | |
COMP 5703 [0.5] | Algorithm Analysis and Design | |
COMP 5704 [0.5] | Parallel Algorithms and Applications in Data Science | |
| 3. 1.0 credit in course work | 1.0 | |
| 4. Fulfillment of the graduate seminar requirement | ||
| 4. 2.5 credits in: | 2.5 | |
COMP 5905 [2.5] | M.C.S. Thesis | |
| Total Credits | 5.0 | |
Notes:
- Course selections must be approved by the student's academic adviser, and must include at least (see OCICS course listing): 0.5 credit in software engineering, 0.5 credit in the theory of computing, and 0.5 credit in either computer applications or computer systems. For students in the Accelerated Stream, the 5000-level COMP courses taken as part of the B.C.S. degree can be used to satisfy area requirements.
- The graduate seminar requirement includes a seminar presentation and participation in at least ten sessions in the joint graduate student seminar series.
- Both course and thesis work may be completed either by full-time or part-time study.
- A candidate can be permitted to carry out thesis work off campus provided suitable arrangements are made for supervision and experimental work, and prior approval is given by the Institute.
Co-operative Option
A co-operative option is also available to full-time students in the Masters of Computer Science. Co-operative education is based on the principle that academic studies combined with work experience are desirable for effective professional preparation.
In addition to all other requirements for the degree, students admitted to the co-operative option must satisfactorily complete two work terms placements with a suitable employer in order to graduate with a co-op designation on their transcripts and diplomas. It is desirable that the work placements be related to the student's research. Placements are subject to the approval of the Supervisor of Graduate Studies and of the student's research supervisor. These work terms are four months in duration and students will conduct job searches through the university's co-op office. During a work term, students will register in COMP 5913.While on a work term, students in this option are limited to taking one additional 0.5-credit course, or registering in their thesis.
Students in the co-op option normally apply for admission to the co-operative option during their first academic term. This option requires an initial study period of two academic terms, typically followed by two work terms and a final academic period to complete the remaining requirements of the degree. The student must submit a work term report upon the completion of each work placement, and receive a grade of Satisfactory in order to meet the requirements for the successful completion of that work term's requirement.
Guidelines for Completion of Master's Degree
The following completion times are estimates only, based on full-time study, and are intended to provide guidance only.
Students are urged to check with the supervisor of graduate studies to determine the exact requirements of the degree program and other related information. Part-time students should calculate the completion times requirement by doubling the time estimates given below.
Students should complete the course work within the first two terms.
Selection of courses should be done in consultation with the student's faculty advisor. Approval from the Graduate Supervisor of the Institute is only required for courses not listed as valid OCICS courses. Subject to the approval of the Graduate Supervisor, M.C.S. students may take courses in other relevant disciplines. At least half of the course credits of an M.C.S. student must be valid OCICS courses. At most, two Fourth Year undergraduate courses may be taken with the permission of the Graduate Supervisor. A thesis supervisor and thesis topic must be selected by the end of the second term. The supervisor of graduate studies should be formally notified of this selection. The expected completion time for the M.C.S. degree is four to six terms of full-time study depending on the type of thesis and the area of research.
Ph.D. Computer Science
About the Program
The Ph.D. program leads to a Doctor of Philosophy degree in Computer Science. The program combines breadth knowledge in computer science through course work and depth knowledge in the area of specialization through research (Ph.D. Thesis option).
The Ph.D. areas of specialization are:
The program emphasizes problems of current practical significance and advances in computer science knowledge. The program fosters collaborative and interdisciplinary work with the scientific and industrial (private and public) communities.
Academic Regulations
See the General Regulations section of this Calendar.
Admission Requirements
Admission to the Ph.D. in Computer Science requires a Masters in Computer Science with thesis, or equivalent including demonstrated significant research ability.
In exceptional cases, students who are currently in the M.C.S. program and who have completed all course requirements with a grade of no less than A in each course may be permitted to transfer into the Ph.D. program.
Program Requirements
| Ph.D. Computer Science (10.0 credits) | ||
| 1. 1.5 credits in courses at the graduate level in at least three different research areas (see OCICS course grouping by area). | 1.5 | |
| The admission committee and the student's advisory committee may impose additional program requirements according to the student's background and research topic. | ||
| 2. Presentation of at least two seminars in the Ottawa-Carleton Institute for Computer Science seminar series: Minimally, the student must make one presentation for the graduate seminar, and one presentation for the departmental seminar. | ||
| 3. 0.0 credit in: | 0.0 | |
COMP 6907 [0.0] | Doctoral Comprehensive (involving breadth and depth components, must be taken within the first 4 terms) | |
| 4. 0.0 credit in: | 0.0 | |
COMP 6908 [0.0] | Doctoral Proposal (defended at an oral examination) | |
| 5. 8.5 credits in: | 8.5 | |
COMP 6909 [8.5] | Ph.D. Thesis ( defended at an oral examination) | |
| Total Credits | 10.0 | |
Guidelines for Completion of Doctoral Degree
The following completion times are estimates based on full-time study.
-
During the first term, the student and his or her faculty adviser should select graduate courses related to their area(s) of research and interests. Approval from the Graduate Supervisor of the Institute is only required for courses not listed as valid OCICS courses.
-
Subject to the approval of the Graduate Supervisor, Ph.D. students may take courses in other relevant disciplines. At least half of the course credits of a Ph.D. student must be valid OCICS courses.
-
An advisory committee comprised of three to five faculty members must be established before the student registers in the comprehensive examination. The committee is responsible for the comprehensive examination, the thesis proposal, and for guiding the student's research. The advisory committee must include at least one representative from EECS at the University of Ottawa. The advisory committee must be approved by the director or associate director of the Institute.
-
All course requirements must be completed within the first 6 terms.
-
Within the first 8 terms, the student must submit a written thesis proposal and defend it in an oral examination (see COMP 6908).
-
The expected completion time for the Ph.D. program is approximately twelve terms depending on the type of thesis and the area of research.
-
Before the completion of the program, the student is expected to present at least two seminars in the Ottawa-Carleton Institute for Computer Science seminar series.
Graduate Courses
Not all of the following courses are offered in a given year. For an up-to-date statement of course offerings or to determine the term of offering, consult central.carleton.ca
Subject to the approval of the Graduate Supervisor, M.C.S. and Ph.D. students may take courses in other relevant disciplines. The courses in the following list are offered by the two departments forming OCICS.
Carleton University
COMP School of Computer Science
University of Ottawa
CSI School of Electrical Engineering and Computer Science
| Software Engineering | ||
| COMP 5001 (CSI 5113) | Foundations of Programming Languages | |
| COMP 5104 (CSI 5314) | Object-Oriented Software Development | |
| COMP 6104 (CSI 7314) | Advanced Topics in Object-Oriented Systems | |
| COMP 6603 (CSI 7161,CSI 7561) | Advanced Topics in Programming Systems and Languages | |
| CSI 5111 (COMP 5501) | Software Quality Engineering | |
| CSI 5112 (COMP 5207) | Software Engineering | |
| CSI 5115 (COMP 5503) | Database Analysis and Design | |
| CSI 5118 (COMP 5302) | Automated Verification and Validation of Software | |
| CSI 5122 (COMP 5301) | Software Usability | |
| CSI 5134 (COMP 5004) | Fault Tolerance | |
| COMP 5209 (CSI 5140) | Visual Analytics | |
| COMP 5110 (CSI 5140) | Computer Security and Usability | |
| SYSC 5101 (ELG 6111) | Design of High Performance Software | |
| SYSC 5103 (ELG 6113) | Software Agents | |
| SYSC 5105 (ELG 6115) | Software Quality Engineering and Management | |
| SYSC 5709 (ELG 6179) | Advanced Topics in Software Engineering | |
| Theory of Computing | ||
| COMP 5003 (CSI 5308) | Principles of Distributed Computing | |
| COMP 5005 (CSI 5390) | Learning Systems for Random Environments | |
| COMP 5008 (CSI 5164) | Computational Geometry | |
| COMP 5107 (CSI 5185) | Statistical and Syntactic Pattern Recognition | |
| COMP 5203 (CSI 5173) | Data Networks | |
| COMP 5306 (CSI 5100) | Data Integration | |
| COMP 5307 (CSI 5101) | Knowledge Representation | |
| COMP 5308 (CSI 5102) | Topics in Medical Computing | |
| COMP 5408 (CSI 5121) | Advanced Data Structures | |
| COMP 5409 (CSI 5127) | Applied Computational Geometry | |
| COMP 5703 (CSI 5163) | Algorithm Analysis and Design | |
| COMP 6601 (CSI 7160) | Advanced Topics in the Theory of Computing | |
| COMP 6602 (CSI 7170,CSI 7970) | Advanced Topics in Distributed Computing | |
| CSI 5108 (COMP 5700) | Software Specification and Verification | |
| CSI 5110 (COMP 5707) | Principles of Formal Software Development | |
| CSI 5126 (COMP 5108) | Algorithms in Bioinformatics | |
| CSI 5148 (COMP 5103) | Wireless Ad Hoc Networking | |
| CSI 5149 (COMP 5007) | Graphical Models | |
| CSI 5161 (COMP 5606) | Topics in System Simulation and Optimization | |
| CSI 5165 (COMP 5709) | Combinatorial Algorithms | |
| CSI 5166 (COMP 5805) | Applications of Combinatorial Optimization | |
| CSI 5169 (COMP 5304) | Wireless Networks and Mobile Computing | |
| CSI 5174 (COMP 5604) | Validation Methods for Distributed Systems | |
| CSI 5510 (COMP 5707) | Principes de developpement formel de logiciels | |
| CSI 5526 (COMP 5108) | Algorithmes en bioinformatique | |
| CSI 5565 (COMP 5709) | Algorithmes combinatoires | |
| COMP 5111 (CSI 5153) | Data Management for Business Intelligence | |
| COMP 5112 (CSI 5154) | Algorithms for Data Science | |
| COMP 5310 (CSI 5140) | Evolving Information Networks | |
| Computer Applications | ||
| COMP 5002 (CSI 5128) | Swarm Intelligence | |
| COMP 5100 (CSI 5180,CSI 5580) | Topics in Artificial Intelligence | |
| COMP 5204 (CSI 5124) | Computational Aspects of Geographic Information Systems | |
| COMP 5206 (CSI 5183) | Evolutionary Computation and Artificial Life | |
| COMP 5305 (CSI 5129) | Advanced Database Systems | |
| COMP 5306 (CSI 5100) | Data Integration | |
| COMP 5307 (CSI 5101) | Knowledge Representation | |
| COMP 5308 (CSI 5102) | Topics in Medical Computing | |
| COMP 5401 (CSI 5389, CSI 5789) | Electronic Commerce Technologies | |
| COMP 5406 (CSI 5105) | Network Security and Cryptography | |
| COMP 5407 (CSI 5116) | Authentication and Software Security | |
| COMP 6604 (CSI 7162) | Advanced Topics in Computer Applications | |
| CSI 5126 (COMP 5108) | Algorithms in Bioinformatics | |
| CSI 5146 (COMP 5202) | Computer Graphics | |
| CSI 5147 (COMP 5201) | Computer Animation | |
| CSI 5151 (COMP 5205) | Virtual Environments | |
| CSI 5168 (COMP 5309) | Digital Watermarking | |
| CSI 5380 (COMP 5405) | Systems and Architectures for Electronic Commerce | |
| CSI 5386 (COMP 5505) | Natural Language Processing | |
| CSI 5387 (COMP 5706) | Data Mining and Concept Learning | |
| CSI 5388 (COMP 5801) | Topics in Machine Learning | |
| CSI 5526 (C0MP 5108) | Algorithmes en bioinformatique | |
| CSI 5580 (COMP 5100) | Sujet en intelligence artificielle | |
| COMP 5110 (CSI 5140) | Computer Security and Usability | |
| COMP 5111 (CSI 5153) | Data Management for Business Intelligence | |
| COMP 5112 (CSI 5154) | Algorithms for Data Science | |
| COMP 5209 (CSI 5140) | Visual Analytics | |
| COMP 5210 (CSI 5140) | Human-Computer Interaction Models, Theories, and Frameworks | |
| COMP 5310 (CSI 5140) | Evolving Information Networks | |
| CSI 5780 (COMP 5405) | Systemes et architectures des logiciels pour le commerce electronique | |
| CSI 5787 (COMP 5706) | Fouille des donnees et apprentissage des concepts | |
| Computer Systems | ||
| COMP 5003 (CSI 5308) | Principles of Distributed Computing | |
| COMP 5101 (CSI 5311) | Distributed Databases and Transaction Processing Systems | |
| COMP 5102 (CSI 5312) | Distributed Operating Systems | |
| COMP 5107 (CSI 5185) | Statistical and Syntactic Pattern Recognition | |
| COMP 5203 (CSI 5173) | Data Networks | |
| COMP 5305 (CSI 5129) | Advanced Database Systems | |
| COMP 5401 (CSI 5389, CSI 5789) | Electronic Commerce Technologies | |
| COMP 5402 (CSI 5142) | Protocols for Mobile and Wireless Networks | |
| COMP 5406 (CSI 5105) | Network Security and Cryptography | |
| COMP 5407 (CSI 5116) | Authentication and Software Security | |
| COMP 5704 (CSI 5131) | Parallel Algorithms and Applications in Data Science | |
| COMP 6100 (CSI 7131) | Advanced Parallel and Systolic Algorithms | |
| COMP 6602 (CSI 7170,CSI 6970) | Advanced Topics in Distributed Computing | |
| COMP 6605 (CSI 7163) | Advanced Topics in Computer Systems | |
| CSI 5134 (COMP 5004) | Fault Tolerance | |
| CSI 5147 (COMP 5201) | Computer Animation | |
| CSI 5148 (COMP 5103) | Wireless Ad Hoc Networking | |
| CSI 5161 (COMP 5606) | Principles of Distributed Simulation | |
| CSI 5168 (COMP 5309) | Digital Watermarking | |
| CSI 5169 (COMP 5304) | Wireless Networks and Mobile Computing | |
| CSI 5174 (COMP 5604) | Validation Methods for Distributed Systems | |
| CSI 5380 (COMP 5405) | Systems and Architectures for Electronic Commerce | |
| CSI 5780 (COMP 5405) | Systemes et architectures des logiciels pour le commerce electronique | |
| Others | ||
| COMP 5900 (CSI 5140) | Selected Topics in Computer Science | |
| COMP 5901 (CSI 5901) | Directed Studies (M.C.S.) | |
| COMP 5903 (CSI 6900) | Intensive Graduate Project (M.C.S.) | |
| COMP 5904 (CSI 5902) | Master's Seminar | |
| COMP 5905 (CSI 7999) | M.C.S. Thesis | |
| COMP 5913 | Master's Co-operative Workterm | |
| COMP 6901 (CSI 7901) | Directed Studies (Ph.D.) | |
| COMP 6902 (CSI 7900) | Graduate Project (Ph.D.) | |
| COMP 6907 (CSI 9998) | Doctoral Comprehensive | |
| COMP 6908 (CSI 9997) | Doctoral Proposal | |
| COMP 6909 (CSI 9999) | Ph.D. Thesis | |
Foundations of Programming Languages
Advanced study of programming paradigms from a practical perspective. Paradigms may include functional, imperative, concurrent, distributed, generative, aspect- and object-oriented, and logic programming. Emphasis on underlying principles. Topics may include: types, modules, inheritance, semantics, continuations, abstraction and reflection.
Swarm Intelligence
Collective computation, collective action, and principles of self-organization in social agent systems. Algorithms for combinatorial optimization problems, division of labour, task allocation, task switching, and task sequencing with applications in security, routing, wireless and ad hoc networks and distributed manufacturing.
Principles of Distributed Computing
Formal models; semantics of distributed computations; theoretical issues in design of distributed algorithms; computational complexity; reducibility and equivalence of distributed problems. Related topics: systolic systems and computations, oligarchical systems and control mechanisms.
Fault Tolerance
Learning Systems for Random Environments
Computerized adaptive learning for random environments and its applications. Topics include a mathematical review, learning automata which are deterministic/stochastic, with fixed/variable structures, of continuous/discretized design, with ergodic/absorbing properties and of estimator families.
Graphic Models
Computational Geometry
Study of design and analysis of algorithms to solve geometric problems; emphasis on applications such as robotics, graphics, and pattern recognition. Topics include: visibility problems, hidden line and surface removal, path planning amidst obstacles, convex hulls, polygon triangulation, point location.
Topics in Artificial Intelligence
Areas in knowledge-based systems including recent approaches to machine learning and data mining, inference methods, knowledge-based and fuzzy systems, heuristic search, and natural language processing.
Distributed Databases and Transaction Processing Systems
Principles in the design and implementation of distributed databases and distributed transaction processing systems. Topics include: distributed computing concepts, computing networks, distributed and multi-database system architectures and models, atomicity, synchronization and distributed concurrency control algorithms, data replication, recovery techniques, reliability in distributed databases.
Distributed Operating Systems
Design issues of advanced multiprocessor distributed operating systems: multiprocessor system architectures; process and object models; synchronization and message passing primitives; memory architectures and management; distributed file systems; protection and security; distributed concurrency control; deadlock; recovery; remote tasking; dynamic reconfiguration; performance measurement, modeling, and system tuning.
Wireless Ad Hoc Networking
Object-Oriented Software Development
Issues in modeling and verifying quality and variability in object-oriented systems. Testable models in model-driven and test-driven approaches. System family engineering. Functional conformance: scenario modeling and verification, design by contract. Conformance to non functional requirements: goals, forces and tradeoffs, metrics.
Statistical and Syntactic Pattern Recognition
Topics include a mathematical review, Bayes decision theory, maximum likelihood and Bayesian learning for parametric pattern recognition, non-parametric methods including nearest neighbor and linear discriminants. Syntactic recognition of strings, substrings, subsequences and tree structures. Applications include speech, shape and character recognition.
Algorithms in Bioinformatics
Computer Security and Usability
This course focuses on designing and evaluating security and privacy software with particular attention to human factors and how interaction design impacts security. Topics include current approaches to usable security, methodologies for empirical analysis, and design principles for usable security and privacy.
Data Management for Business Intelligence
Application of computational techniques to support business such as decision making, business understanding, data analysis, business process automation, learning from data, producing and using business models, data integration, data quality assessment and cleaning, use of contextual data, etc.
Algorithms for Data Science
Algorithmic techniques to handle (massive/big) data arising from, for example, social media, mobile devices, sensors financial transactions. Algorithmic techniques may include locality-sensitive hashing, dimensionality reduction, streaming, clustering, VC-dimensions, external memory, core sets, link analysis and recommendation systems.
Computer Animation
Computer Graphics
Data Networks
Mathematical and practical aspects of design and analysis of communication networks. Topics include: basic concepts, layering, delay models, multi-access communication, queuing theory, routing, fault-tolerance, and advanced topics on high-speed networks, ATM, mobile wireless networks, and optical networks.
Computational Aspects of Geographic Information Systems
Computational perspective of geographic information systems (GIS). Data representations and their operations on raster and vector devices: e.g., quadtrees, grid files, digital elevation models, triangular irregular network models. Analysis and design of efficient algorithms for solving GIS problems: visibility queries, point location, facility location.
Virtual Environments
Evolutionary Computation and Artificial Life
Study of algorithms based upon biological theories of evolution, applications to machine learning and optimization problems. Possible topics: Genetic Algorithms, Classifier Systems, and Genetic Programming. Recent work in the fields of Artificial Life (swarm intelligence, distributed agents, behavior-based AI) and of connectionism.
Software Engineering
Visual Analytics
Principles, techniques, technology and applications of information visualization for data analysis. Topics include human visual perception, cognitive processes, static and dynamic models of image semantics, interaction paradigms, big data visual analysis case studies.
Human-Computer Interaction Models, Theories, and Frameworks
Emphasis on the application of theory to user interface design. Review of main theories of human behaviour relevant to HCI, including especially cognitive dimensions of notations framework, mental models, distributed cognition, and activity theory, and their application to design and development of interactive systems.
Software Usability
Automated Verification & Validation of Software
Wireless Networks and Mobile Computing
Advanced Database Systems
In-depth study on developments in database systems shaping the future of information systems, including complex object, object-oriented, object-relational, and semi-structured databases. Data structures, query languages, implementation and applications.
Data Integration
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.
Knowledge Representation
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.
Topics in Medical Computing
Introductory course on data structures, algorithms, techniques, and software development related to medical computing (in particular spatial modeling). Topics may include: computational geometry algorithms for cancer treatment, medical imaging, spatial data compression algorithms, dynamic programming for DNA analysis.
Digital Watermarking
Evolving Information Networks
Convergence of social and technological networks with WWW. Interplay between information content, entities creating it and technologies supporting it. Structure and analysis of such networks, models abstracting their properties, link analysis, search, mechanism design, power laws, cascading, clustering and connections with work in social sciences.
Electronic Commerce Technologies
Introduction to business models and technologies. Search engines. Cryptography. Web services and agents. Secure electronic transactions. Value added e-commerce technologies. Advanced research questions.
Protocols for Mobile and Wireless Networks
Link and network layer protocols of wireless networks; applications of wireless networks may be discussed. Topics may include: protocol implementation, mobile IP, resource discovery, wireless LANs/PANs, and Spreadspectrum.
Systems and Architectures for Electronic Commerce
Network Security and Cryptography
Advanced methodologies selected from symmetric and public key cryptography, network security protocols and infrastructure, identification, secret-sharing, anonymity, intrusion detection, firewalls, defending network attacks and performance in communication networks.
Authentication and Software Security
Specialized topics in security including advanced authentication techniques, user interface aspects, electronic and digital signatures, security infrastructures and protocols, software vulnerabilities affecting security, untrusted software and hosts, protecting software and digital content.
Advanced Data Structures
Simple methods of data structure design and analysis that lead to efficient data structures for several problems. Topics include randomized binary search trees, persistence, fractional cascading, self-adjusting data structures, van Emde Boas trees, tries, randomized heaps, and lowest common ancestor queries.
Applied Computational Geometry
Computer-based representation and manipulation of geometric objects. Design and analysis of efficient algorithms for solving geometric problems in applied fields such as Computer-Aided Design and Manufacturing, Cartography, Materials Science, and Geometric Network Design.
Software Quality Engineering
Database Analysis & Design
Natural Language Processing
Validation Methods for Distributed Systems
Topics in Simulation and Optimization
Algorithm Analysis and Design
Topics of current interest in the analysis and design of sequential and parallel algorithms for non-numerical, algebraic and graph computations. Lower bounds on efficiency of algorithms. Complexity classes.
Parallel Algorithms and Applications in Data Science
Multiprocessor architectures from an application programmer’s perspective: programming models, processor clusters, multi-core processors, GPU’s, 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.
Data Mining & Concept Learning
Principles of Formal Software Development
Combinatorial Algorithms
Topics in Machine Learning
Applications of Combinatorial Optimization
Selected Topics in Computer Science
Selected topics, not covered by other graduate courses. Details will be available from the School at the time of registration.
Directed Studies (M.C.S.)
A course of independent study under the supervision of a member of the School of Computer Science.
Intensive Graduate Project (M.C.S.)
A one- or two-session course. For M.C.S. non-thesis option students only. Not to be combined for credit with COMP 5902.
Master's Seminar
To complete this course, the student must attend 5 graduate seminars at Carleton, and 5 at SITE within a year. The student must also make one presentation in the context of this graduate seminar.
M.C.S. Thesis
Master's Co-operative Workterm
Advanced Parallel and Systolic Algorithms
Continuation of COMP 5704.
Advanced Topics in Object-Oriented Systems
Advanced object-oriented software engineering, in particular the issues of reuse and testing. Sample topics include: interaction modeling; class and cluster testing; traceability; design patterns and testing; the C++ standard template library. Students will carry out research.
Advanced Topics in the Theory of Computing
Advanced Topics in Distributed Computing
Advanced Topics in Programming Systems and Languages
Advanced Topics in Computer Applications
Advanced Topics in Computer Systems
Directed Studies (Ph.D.)
Graduate Project (Ph.D.)
Doctoral Comprehensive
A committee must be assembled and approve at least 3 topics for written examination: typically, a major and two minor areas. An oral examination occurs if the written exam is passed. Both elements must take place within the first 4 terms following initial registration in the program. The comprehensive may be failed, passed conditionally (i.e., with extra course requirements) or passed unconditionally. If failed this course may be retaken at most one time.
Doctoral Proposal
Within 8 terms following initial registration in the program, a document generally defining the problem addressed, relating it to the literature, and outlining the hypotheses, goals, research methodology, initial results and validation approach must be submitted to an examination committee and successfully defended.
Ph.D. Thesis
Summer session: some of the courses listed in this Calendar are offered during the summer. Hours and scheduling for summer session courses will differ significantly from those reported in the fall/winter Calendar. To determine the scheduling and hours for summer session classes, consult the class schedule at central.carleton.ca
Not all courses listed are offered in a given year. For an up-to-date statement of course offerings for the current session and to determine the term of offering, consult the class schedule at central.carleton.ca