IX. Graduate Programs
Computer Science
Courses
Core Courses
The core graduate courses are designed to be accessible to any student with an appropriate background in Computer Science and will provide enough introduction for those unfamiliar with the specific area to allow them to keep up with the advanced material.
| CIS*6000 Distributed Systems U [0.50] |
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| The evolution of distributed computer systems. Models for distributed processing. Taxonomy of multiprocessor systems. Interconnection networks. Memory and I/O for distributed architectures. Performance of distributed systems. Architectural issues of distributed systems |
| CIS*6020 Artificial Intelligence U [0.50] |
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| An examination of Artificial Intelligence principles and techniques such as: logic and rule based systems; forward and backward chaining; frames, scripts, semantic nets and the object-oriented approach; the evaluation of intelligent systems and knowledge acquisition. A sizeable project is required and applications in other areas are encouraged. |
| CIS*6030 Information Systems U [0.50] |
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| Relational and other database systems, web information concurrency protocols, data integrity, transaction management, distributed databases, remote access, data warehousing, data mining. |
| CIS*6070 Discrete Optimization U [0.50] |
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| This course will discuss problems where optimization is required and describes the most common techniques for discrete optimization such as the use of linear programming, constraint satisfaction methods, and genetic algorithms. |
| CIS*6320 Image Processing Algorithms and Applications U [0.50] |
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| Brightness transformation, image smoothing, image enhancement, thresholding, segmentation, morphology, texture analysis, shape analysis, applications in medicine and biology. |
| CIS*6420 Soft Computing U [0.50] |
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| Neural networks, artificial intelligence, connectionist model, back propagation, resonance theory, sequence processing, software engineering concepts. |
| CIS*6890 Technical Communication and Research Methodology U [0.50] |
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| This course aims to develop students' ability in technical communication and general research methodology. Each student is expected to present a short talk, give a mini lecture, review a conference paper, write a literature survey and critique fellow students' talks and lectures. |
Advanced Courses
The advanced graduate courses are taught with the assumption that the student has sufficient background in the research area to understand the advanced concepts and research ideas. Students who intend to take a course for which they have insufficient background should consult with the instructor prior to enrollment in the course.
| CIS*6050 Neural Networks U [0.50] |
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| Artificial neural networks, dynamical recurrent networks, dynamic input/output sequences, communications signal identification, syntactic pattern recognition. |
| CIS*6060 Bioinformatics U [0.50] |
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| Data mining and bioinformatics, molecular biology databases, taxonomic groupings, sequences, feature extraction, Bayesian inference, cluster analysis, information theory, machine learning, feature selection. |
| CIS*6080 Genetic Algorithms U [0.50] |
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| This course introduces the student to basic genetic algorithms, which are based on the process of natural evolution. It is explored in terms of its mathematical foundation and applications to optimization in various domains. |
| CIS*6090 Hardware/Software Co-design of Embedded Systems U [0.50] |
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| Specification and design of embedded systems, system-on-a-chip paradigm, specification languages, hardware/software co-design, performance estimation, co-simulation and validation, processes architectures and software synthesis, retargetable code generation and optimization. |
| CIS*6100 Parallel Processing Architectures U [0.50] |
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| Parallelism in uniprocessor systems, parallel architectures, memory structures, pipelined architectures, performance issues, multiprocessor architectures. |
| CIS*6120 Uncertainty Reasoning in Knowledge Representation U [0.50] |
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| Representation of uncertainty, Dempster-Schafer theory, fuzzy logic, Bayesian belief networks, decision networks, dynamic networks, probabilistic models, utility theory. |
| CIS*6130 Object-Oriented Modeling, Design and Programming U [0.50] |
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| Objects, modeling, program design, object-oriented methodology, UML, CORBA, database |
| CIS*6140 Software Engineering U [0.50] |
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| This course will discuss problems where optimization is required and describes the most common techniques for discrete optimization such as the use of linear programming, constraint satisfaction methods, and meta-heuristics. |
| CIS*6160 Multiagent Systems U [0.50] |
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| Intelligent systems consisting of multiple autonomous and interacting subsystems with emphasis on distributed reasoning and decision making. Deductive reasoning agents, practical reasoning agents, probabilistic reasoning agents, reactive and hybrid agents, negotiation and agreement, cooperation and coordination, multiagent search, distributed MDP, game theory, and modal logics. |
| CIS*6200 Design Automation in Digital Systems U [0.50] |
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| Techniques and software tools for design of digital systems. Material covered includes high-level synthesis, design for testability, and FPGAs in design and prototyping. |
| CIS*6490 Analysis and Design of Computer Algorithms U [0.25] |
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| The design and analysis of efficient computer algorithms: standard methodologies, asymptotic behaviour, optimality, lower bounds, implementation considerations, graph algorithms, matrix computations (e.g. Strassen's method), NP-completeness. |
| CIS*6650 Topics in Computer Science I U [0.50] |
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| This special topics course examines selected, advanced topics in computer science that are not covered by existing courses. The topic(s) will vary depending on the need and the instructor. |
| CIS*6660 Topics in Computer Science II U [0.50] | |
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| This is a reading course. Its aim is to provide background knowledge to students who need to get a head-start in their thesis research fields early during their program while no suitable regular graduate courses are offered. Admission is under the discretion of the instructor. | |
| Restriction(s): | Requires instructor's signature. |