Course subject: Computer Science (CS)

An overview of basic security and privacy principles relevant in the design and use of applications in health settings. Program security, operating system security, network security, data security, and issues related to the management of security and privacy policies are introduced. Master of Health Informatics students only.

A user-oriented approach to the management of large collections of data. Relational database technology, relational algebra, SQL, database views, transactions, data modelling methodology, entity-relationship models. Introduction to several current topics in database research, such as warehousing, data mining, managing data streams, data cleaning, data integration, and distributed/parallel databases. Master of Health Informatics students only.

Phases of compilation. Lexical analysis and a review of parsing. Compiler-compilers and translator writing systems. LEX and YACC. Scope rules, block structure, and symbol tables. Runtime stack management. Parameter passage mechanisms. Stack storage organization and templates. Heap storage management. Intermediate code. Code generation. Macros.

Introduces students to the design, implementation, and evolution phases of software development. Software design processes, methods, and notation. Implementation of designs. Evolution of designs and implementations. Management of design activities.

The objective of this course is to introduce students to fundamentals of building a relational database management system. The course focuses on the database engine core technology by studying topics such as storage management (data layout, disk-based data structures), indexing, query processing algorithms, query optimization, transactional concurrency control, logging and recovery. Preference will be given to CS graduate students. All other require permission from the school.

The course is intended to provide the student with an appreciation of modern computer design and its relation to system architecture, compiler technology and operating system functionality. The course places an emphasis on design based on the measurement of performance and its dependency on parallelism, efficiency, latency and resource utilization.

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.

Basic concepts and implementation of numerical linear algebra techniques and their use in solving application problems. Special methods for solving linear systems having special features. Direct methods: symmetric, positive definite, band, general sparse structures, ordering methods. Iterative methods: Jacobi, Gauss-Seidel, SOR, conjugate gradient. Computing and using orthogonal factorizations of matrices. QR and SVD methods for solving least squares problems. Eigenvalue and singular value decompositions. Computation and uses of these decompositions in practice.

Introduction to modeling and algorithmic techniques for machines to learn concepts from data. Generalization: underfitting, overfitting, cross-validation. Tasks: classification, regression, clustering. Optimization-based learning: loss minimization, regularization. Statistical learning: maximum likelihood, Bayesian learning. Algorithms: nearest neighbor, (generalized) linear regression, mixtures of Gaussians, Gaussian processes, kernel methods, support vector machines, deep learning, sequence learning, ensemble techniques. Large scale learning: distributed learning and stream learning. Applications: Natural language processing, computer vision, data mining, human computer interaction, information retrieval.

Introduction to image and vision understanding by computer. Camera-system geometry, image formation and lighting, and image acquisition. Basic visual processes for recognition of edges, regions, lines, and surfaces. Processing of stereo images, and motion in image sequences. Object recognition. Applications of computer vision systems.

Goals and methods of artificial intelligence. Methods of general problem solving. Introduction to mathematical logic Mechanical theorem proving. Game playing. Natural language processing. Preference will be given to CS graduate students. All others require approval from the department. Department approval will be by Undergraduate Advisor.

Software and hardware for interactive computer graphics. Implementation of device drivers, 3-D transformations, clipping, perspective, and input routines. Data structures, hidden surface removal, colour shading techniques, and some additional topics will be covered. Preference will be given to CS graduate students. All others require approval from the department.

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 grade course. Department permission will be by Coordinator of Graduate Studies.

Research and life skills that can help graduate students improve their academic and interpersonal competence-research methodologies, library research skills, creative and critical thinking, time management, stress management, technical reading skills, listening skills, oral communication, writing and publishing in computer science, jobs in academica versus industry.

Project-oriented course that covers the implementation of relational database management systems. Topics include database system architecture; managing primary and secondary storage; query processing; metadata and catalog management; language processing; query optimization and plan generation; concurrency; failures and recovery; extensibility; client-server interactions.

Algorithms and architectures used in parallel database management systems, with a focus on relational systems. Topics include system architectures; parallel and distributed query processing; federated dtabase systems; distributed transactions; data replication.

Project-oriented course that covers optimizing compilers and the implementation of advanced programming language features. Topics include intermediate representations; data-flow, dependence, and alias analysis; optimizing transforms, register allocation, instruction scheduling; memory management, garbage collection, threads, concurrency; single and multiple inheritance, generics, templates, type inference.

A project-oriented course that covers the concepts in software architecture. Topics include basic concepts (components and connectors, rationale, views, architectural diagrams, team implictions, evolution, size considerations), theory underlying softeware architecture, extraction of architecture from implementation artifacts, architecture of web-based and enterprise systems, clustering of subsystems, visualization approaches to software architecture, comprehension and cognitive aspects of software architecture.

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.

Linear optimization: Farkas' Lemma, duality, Simplex method, geometry of polyhedra. Combinatorial optimization: integrality of polyhedra, total unimodularity, flow problems, weighted bipartite matching. Continuous optimization: convex sets, Separation Theorem, convex functions, analytic characterizations of convexity, Karush-Kuhn-Tucker Theorem.