Course subject: Computer Science (CS)

An introduction to computer networks, Internet protocols, and distributed systems. Layered models, naming, addressing and routing, reliable communication, security, and elements of distributed system design. Master of Health Informatics students only.

An exposure to important concepts and issues in contemporary programming languages. Data types, abstraction, and polymorphism. Program structure. Lambda calculus and functional programming, logic programming, object-oriented programming. Semantics of programming languages. Critical comparison of language features and programming methodologies using examples drawn from a variety of programming languages including Lisp, Prolog, ML, Ada, Smalltalk, Icon, APL, and Lucid. Programming assignments involve the use of some of these languages.

Introduces students to the requirements definition phase of software development: Models, notations, and processes for software requirements identification, representation, analysis, and validation. Cost estimation from early documents and specifications.

Introduces students to systematic testing of software systems. Software verification, reviews, metrics, quality assurance, and prediction of software reliability and availability. Related management issues.

An introduction to the fundamental theories, methods, and research in the design and evaluation of description: novel computational artifacts designed to meet real-world human needs.

Intended to give students experience with tools and techniques of real-time programming, this course includes not only issues of microcomputer architecture and a real-time programming language and operating system, but also hands-on experience programming a microcomputer for applications such as process control, data acquisition and communication. .Preference will be given to CS graduate students. All others require approval from the department.

An introduction to network architectures and protocols, placing emphasis on protocols used in the Internet. Specific topics include application layer protocols, network programming, transport protocols, routing, multicast, data link layer issues, multimedia networking, network security, and network management.

Security and privacy issues in various aspects of computing. Specific topics include: comparing security and privacy, program security, writing secure programs, controls against program threats, operating system security, formal security models, network security, Internet application security and privacy, privacy-enhancing technologies, database security and privacy, inference data mining, security policies, physical security, economics of security, and legal and ethical issues. (Note: Knowledge of operating systems equivalent to that obtained from CS 350 is assumed.)

The classification of problems according to the computational resources required for their solution, with emphasis on properties of feasible computations rather than on specific algorithms. Topics include: time and space complexity, tractable and intractable problems, computation using randomness, parallel computation.

An introduction to computational problems in medical imaging. Sources of medical images (MRI, CT, ultrasound, PET) as well as reconstruction methods for MRI and CT. Image manipulation and enhancement such as denoising and deblurring. Patient motion correction and optimal image alignment. Tissue classification and organ delineation using image topology. (Note: Lab is not scheduled and students are expected to find time in open hours to complete their work. Offered: W)

The interaction of financial modes, numerical methods, and computing environments. Basic computational aspects of option pricing and hedging. Numerical methods for stochastic differential equations, strong and weak convergence. Generating correlated random numbers. Time-stepping methods. Finite difference methods for Black-Scholes equation. Discretization, stability, convergence. Methods for portfolio optimization, effect of data errors on portfolio weights. (Heldwith CS 476).

Algorithms and techniques used in the identification and functional characterization of cellular proteins. Topics include: protein databases, gene expression analysis, protein structure prediction, protein function prediction, active site detection and ligand docking, protein-protein interaction, HTCS (High Throughput Conformational Search), and QSAR (Quantitative Structure-Activity Relationships). (Heldwith CS 483).

Extracting meaningful patterns from random samples of large data sets. Statistical analysis of the resulting problems. Common algorithm paradigms for such tasks. Central concepts: VC-dimension, Margins of classifier, Sparsity and description length. Performance guarantees: Generalization bounds, data dependent error bounds and computational complexity of learning algorithms. Common paradigms: Neural networks, Kernel methods and Support Vector machines, Applications to Data Mining.

An introduction to the use of computers for symbolic mathematical computation, involving traditional mathematical computations such as solving linear equations (exactly), analytic differentiation and integration of functions, and analytic solution of differential equations.

This is an individual study course carried out under the supervision of a Computer Science faculty member. The topic should be agreed upon by both the student and the instructor. This is a credit/no credit course. Department permission will be by Coordinator of Graduate Studies.

This course is designed to consider the problems encountered by individuals, organizations and society as computer technology is adopted, with a view towards assessing possible courses of action.

This number is used for courses being offered on a temporary basis. Such a course may be available only once, for example to take advantage of a visiting professor's expertise, or may be offered experimentally until it is determined whether of not the course should become part of the regular course offerings. It may also be used for an individual study course carried out under the supervision of a Computer Science faculty member with the approval from the Associate Chair, Graduate Studies. This is a grade course. Preference will be given to CS graduate students. All others require approval of the Department.

Management of non-relational databases, such as multimedia databases, text databases, temporal databases or spatial databases. Each offering will target a specific type of data. Topics include rationale for and common applications of non-relational database management; systems and standards; the abstract data model; data definition and manipulation languages; data storage and indexing; query processing and optimization; updates and transaction management.

An overview of relational databases and how they are used; exposure to relational database technology. Fundamentals of transactions. Database views. Introductions to two or three alternative data models and systems, such as those used for structured text, spatial data, multimedia data, and information retrieval. Introduction to several current topics in database research, such as warehousing, data mining, managing data streams, data cleaning, data integration, and distributed databases.

Application of formal methods to the verification of computer-based systems. Algebraic and automata preliminaries. Temporal logic and model checking. Decision procedures. Mechanized theorem proving. Advanced topics chosen by the instructor.

Distributed, multi-user applications are designed and implemented using many underlying technologies that must be coordinated to provide important features such as robustness, scalability, manageability, ubiquitous access, privacy, security, authentication, and role-based access control, to name only a few. The network supporting the application may be crucial to its successful implementation. The application logic itself is likely implemented in a number of languages and programming environments. Students will be provided with an advanced overview of current networking and distributed systems topics, and will apply it to case studies drawn form consumer internet applications, enterprise systems, and medical and healthcare systems.

Introduction to the design and analysis of algorithms that make use of randomization. Topics include review of basic probabiloity and introduction to randomized algorithms; game theoretic techniques; uses of Markov and Chebyshev inequalities; tail inequalities; Markov chains and random walks; algebraic techniques; data structures and graph algorithms.

Introduction to the design, analysis and application of algorithms for geometric problems. Topics include convex hull algorithms in two and three dimensions; Voronoi diagrams, Delaunay triangulations, and their applications; linear programming in low dimensions; line segments, planar subdivision, and polygons; range searching.

Fundamental problems of elementary and algebraic number theory from an algorithmic and computational complexity point of view with emphasis placed on analysis of algorithms. Topics include basic arithmetic algorithms; computation over finite fields; primality testing; algorithms for integer factorization; algorithms in number fields.

The course provides extended background in mathematical logic and its applications to various branches of computer science. It covers some fundamental concepts such as soundness and completeness theorems, compactness, the expressive power of a logic and the computational complexity of its basic decision problems. These concepts are being demonstrated on first order logic and modal logics. Finally the course discusses examples of applications of these concepts and tools to formal reasoning about programs and about hardware, to data bases, and to knowledge representation.

Introduction to basic algorithms and techniques for numerical computing. Error analysis, interpolation (including splines), numerical differentiation and integration, numerical linear algebra (including methods for linear systems, eigenvalue problems, and the singular value decomposition), root finding for nonlinear equations and systems, numerical ordinary differential equations, and approximation methods (including least squares, orthogonal polynomials, and Fourier transforms).

Techniques for obtaining maximum parallelism in various numberical algorithms, especially those occurring when solving matrix problems and partial differential equations, and the subsequent mapping onto the computer. Topics include: parallel architecture and performance models; message passing/shared memory programming; matrix computations; fast Fourier transform; graph partitioning; domain decomposition methods.

Spline theory and recent developments in techniques for representing, manipulating and rendering curves and surfaces constructed from splines in a graphic environment. Applications of interest include computer-aided design, synthetic image generation and animation.

In computer graphics, 3D models are rendered through an ideal camera to a 2D image. The virtual camera is a powerful tool, controlling not only perspective and the optical path, but colour mapping as well. This course examines the notion of "colour" at different stages of the computer graphics pipeline, and describes techniques for modelling and managing colour through the virtual camera analogy. Topics include illumination, the virtual camera, colour spaces, gamut mapping and colour management. Implementation of the colour reproduction and gamut-mapping algorithms demands a number of numerical and statistical methods, including multidimensional interpolation and approximation, computational geometry, splines, principal components anlysis and optimization.

This course addresses the computer (in silico) simulation of biophysical processes involved in the interaction of light with organic and inorganic matter. Computer models used to simulate these processes are presented, and key stages of the simulation pipeline, such as data gathering, design constraints and evaluation methodologies, are examined in detail. This course also includes discussions of open problems and current trends in the computer simulation of biophysical processes. Application fields include, but are not limited to computer graphics, remote sensing, biology and biomedical optics.

Intelligence in interfaces-natural language processing, plan recognition, dialogue, generation, user modeling. Interfaces to intelligent systems-intelligent agents and multi-agent systems, information processing and data mining, knowledge-based systems.

Fundamental problems in computational vision where efficient and robust algorithms can be applied. Topics include image formation; linear systems and Fourier theory; image registration; feature detection; fitting models to data; optical flow; structure from motion; steriopsis; object recognition; high-level vision.

Graphics input devices and interaction techniques. The pragmatic factors of various physical, logical and virtual devices, human factors of interactive systems, interaction dialogue managers.

This course focuses on health data as a key component of all health informatics systems. Topics include ontologies and other classification taxonomies found in health systems, data standards (with a focus on Canadian implementations of international standards), privacy and security of health data, client/patient assessment tools, and ethical considerations.

The course description will vary according to the topic offered under this course title.