Appendix A - Courses
Computing and Information Science
| CIS*6000 Distributed Systems U [0.50] | |
|---|---|
| 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 | |
| Department(s): | School of Computer Science |
| CIS*6020 Artificial Intelligence U [0.50] | |
|---|---|
| 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. | |
| Department(s): | School of Computer Science |
| CIS*6030 Information Systems U [0.50] | |
|---|---|
| Relational and other database systems, web information concurrency protocols, data integrity, transaction management, distributed databases, remote access, data warehousing, data mining. | |
| Department(s): | School of Computer Science |
| CIS*6050 Neural Networks U [0.50] | |
|---|---|
| Artificial neural networks, dynamical recurrent networks, dynamic input/output sequences, communications signal identification, syntactic pattern recognition. | |
| Department(s): | School of Computer Science |
| CIS*6060 Bioinformatics U [0.50] | |
|---|---|
| Data mining and bioinformatics, molecular biology databases, taxonomic groupings, sequences, feature extraction, Bayesian inference, cluster analysis, information theory, machine learning, feature selection. | |
| Department(s): | School of Computer Science |
| CIS*6070 Discrete Optimization U [0.50] | |
|---|---|
| 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. | |
| Department(s): | School of Computer Science |
| CIS*6080 Genetic Algorithms U [0.50] | |
|---|---|
| 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. | |
| Department(s): | School of Computer Science |
| CIS*6090 Hardware/Software Co-design of Embedded Systems U [0.50] | |
|---|---|
| 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. | |
| Department(s): | School of Computer Science |
| CIS*6100 Parallel Processing Architectures U [0.50] | |
|---|---|
| Parallelism in uniprocessor systems, parallel architectures, memory structures, pipelined architectures, performance issues, multiprocessor architectures. | |
| Department(s): | School of Computer Science |
| CIS*6120 Uncertainty Reasoning in Knowledge Representation U [0.50] | |
|---|---|
| Representation of uncertainty, Dempster-Schafer theory, fuzzy logic, Bayesian belief networks, decision networks, dynamic networks, probabilistic models, utility theory. | |
| Department(s): | School of Computer Science |
| CIS*6130 Object-Oriented Modeling, Design and Programming U [0.50] | |
|---|---|
| Objects, modeling, program design, object-oriented methodology, UML, CORBA, database | |
| Department(s): | School of Computer Science |
| CIS*6140 Software Engineering U [0.50] | |
|---|---|
| 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. | |
| Department(s): | School of Computer Science |
| CIS*6160 Multiagent Systems U [0.50] | |
|---|---|
| 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. | |
| Department(s): | School of Computer Science |
| CIS*6200 Design Automation in Digital Systems U [0.50] | |
|---|---|
| Techniques and software tools for design of digital systems. Material covered includes high-level synthesis, design for testability, and FPGAs in design and prototyping. | |
| Department(s): | School of Computer Science |
| CIS*6320 Image Processing Algorithms and Applications U [0.50] | |
|---|---|
| Brightness transformation, image smoothing, image enhancement, thresholding, segmentation, morphology, texture analysis, shape analysis, applications in medicine and biology. | |
| Department(s): | School of Computer Science |
| CIS*6420 Soft Computing U [0.50] | |
|---|---|
| Neural networks, artificial intelligence, connectionist model, back propagation, resonance theory, sequence processing, software engineering concepts. | |
| Department(s): | School of Computer Science |
| CIS*6490 Analysis and Design of Computer Algorithms U [0.25] | |
|---|---|
| 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. | |
| Department(s): | School of Computer Science |
| CIS*6650 Topics in Computer Science I U [0.50] | |
|---|---|
| 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. | |
| Department(s): | School of Computer Science |
| CIS*6660 Topics in Computer Science II U [0.50] | |
|---|---|
| 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): | Instructor consent required. |
| Department(s): | School of Computer Science |
| CIS*6890 Technical Communication and Research Methodology U [0.50] | |
|---|---|
| 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. | |
| Department(s): | School of Computer Science |