Computing Science Courses

CMPT 102 - Introduction to Scientific Computer Programming (3)

A programming course which will provide the science student with a working knowledge of a scientific programming language and an introduction to computing concepts, structured programming, and modular design. The student will also gain knowledge in the use of programming environments including the use of numerical algorithm packages. Corequisite: MATH 152 or 155 (or 158). Students with credit for CMPT 120, 128, 130 or 166 may not take this course for further credit. Students who have taken CMPT 125, 129 or 135 first may not then take this course for further credit. Quantitative.

CMPT 105W - Social Issues and Communication Strategies in Computing Science (3)

This course teaches the fundamentals of informative and persuasive communication for professional engineers and computer scientists. A principal goal of this course is to assist students in thinking critically about various contemporary technical, social, and ethical issues. It focuses on communicating technical information clearly and concisely, managing issues of persuasion when communicating with diverse audiences, presentation skills, and teamwork. Students with credit for ENSC 102, ENSC 105W, MSE 101W or SEE 101W may not take CMPT 105W for further credit. Writing.

CMPT 106 - Applied Science, Technology and Society (3)

Reviews the different modes of thought characteristic of science, engineering and computing. Examines the histories and chief current research issues in these fields. Considers the ethical and social responsibilities of engineering and computing work. Students with credit for ENSC 100, ENSC 106 or MSE 102 cannot take this course for further credit.

CMPT 110 - Programming in Visual Basic (3)

Topics will include user interfaces, objects, event-driven programming, program design, and file and data management. Prerequisite: BC Mathematics 12 (or equivalent) or any 100-level MATH course with a minimum grade of C-. Students who have obtained credit for, or are currently enrolled in a computing science course at the 200-level or higher, or ITEC 240, 241 or 242 may not take CMPT 110 for further credit except with permission of the School of Computing Science. Quantitative.

CMPT 115 - Exploring Computer Science (3)

You will be exploring fundamental ideas in Computer Science, and the far-reaching impact of computational thinking on modern society. Programming is not the focus, but you will do a bit of programming to actively experience the computational point of view on the world, creating applications in AI and robotics using friendly visual programming languages. No prior computing experience required. Students who have taken CMPT 120, 125, 127, 130, 135 or 170, or IAT 265 or 267 first may not then take this course for further credit. Breadth-Science.

CMPT 118 - Special Topics in Computer and Information Technology (3)

Special topics in computing science which are of current interest to non-computing students. The course will be offered from time to time depending on availability of faculty and on student interest. Students who have obtained credit for, or are currently enrolled in a computing science course at the 200-level or higher, may not take CMPT 118 for further credit.

CMPT 120 - Introduction to Computing Science and Programming I (3)

An elementary introduction to computing science and computer programming, suitable for students with little or no programming background. Students will learn fundamental concepts and terminology of computing science, acquire elementary skills for programming in a high-level language, e.g. Python. The students will be exposed to diverse fields within, and applications of computing science. Topics will include: pseudocode; data types and control structures; fundamental algorithms; recursion; reading and writing files; measuring performance of algorithms; debugging tools; basic terminal navigation using shell commands. Treatment is informal and programming is presented as a problem-solving tool. Prerequisite: BC Math 12 or equivalent is recommended. Students with credit for CMPT 102, 128, 130 or 166 may not take this course for further credit. Students who have taken CMPT 125, 129, 130 or 135 first may not then take this course for further credit. Quantitative/Breadth-Science.

CMPT 125 - Introduction to Computing Science and Programming II (3)

A rigorous introduction to computing science and computer programming, suitable for students who already have some background in computing science and programming. Intended for students who will major in computing science or a related program. Topics include: memory management; fundamental algorithms; formally analyzing the running time of algorithms; abstract data types and elementary data structures; object-oriented programming and software design; specification and program correctness; reading and writing files; debugging tools; shell commands. Prerequisite: CMPT 120 or CMPT 130, with a minimum grade of C-. Students with credit for CMPT 126, 129, 135 or CMPT 200 or higher may not take this course for further credit. Quantitative.

CMPT 128 - Introduction to Computing Science and Programming for Engineers (3)

An introduction to computing science and computer programming, suitable for students wishing to major in Engineering Science or a related program. This course introduces basic computing science concepts, and fundamentals of object oriented programming. Topics include: fundamental algorithms and problem solving; abstract data types and elementary data structures; basic object-oriented programming and software design; elements of empirical and theoretical algorithmics; computation and computability; specification and program correctness; and history of computing science. The course will use a programming language commonly used in Engineering Science. Prerequisite: BC Math 12 (or equivalent, or any of MATH 100, 150, 151, 154, or 157, with a minimum grade of C-). Students with credit for CMPT 102, 120, 130 or 166 may not take this course for further credit. Students who have taken CMPT 125, 129, 135, or CMPT 200 or higher first may not then take this course for further credit. Quantitative/Breadth-Science.

CMPT 129 - Introduction to Computing Science and Programming for Mathematics and Statistics (3)

A second course in computing science and programming intended for students studying mathematics, statistics or actuarial science and suitable for students who already have some background in computing science and programming. Topics include: a review of the basic elements of programming: use and implementation of elementary data structures and algorithms; fundamental algorithms and problem solving; basic object-oriented programming and software design; computation and computability and specification and program correctness. Prerequisite: CMPT 102 or CMPT 120, with a minimum grade of C-. Students with credit for CMPT 125 or 135 may not take this course for further credit. Quantitative.

CMPT 130 - Introduction to Computer Programming I (3)

An introduction to computing science and computer programming, using a systems oriented language, such as C or C++. This course introduces basic computing science concepts. Topics will include: elementary data types, control structures, functions, arrays and strings, fundamental algorithms, computer organization and memory management. Prerequisite: BC Math 12 (or equivalent, or any of MATH 100, 150, 151, 154, or 157, with a minimum grade of C-). Students with credit for CMPT 102, 120, 128 or 166 may not take this course for further credit. Students who have taken CMPT 125, 129 or 135 first may not then take this course for further credit. Quantitative/Breadth-Science.

CMPT 135 - Introduction to Computer Programming II (3)

A second course in systems-oriented programming and computing science that builds upon the foundation set in CMPT 130 using a systems-oriented language such as C or C++. Topics: a review of the basic elements of programming; introduction to object-oriented programming (OOP); techniques for designing and testing programs; use and implementation of elementary data structures and algorithms; introduction to embedded systems programming. Prerequisite: CMPT 130 with a minimum grade of C-. Students with credit for CMPT 125, 126, or 129 may not take this course for further credit. Quantitative.

CMPT 166 - An Animated Introduction to Programming (3)

An informal introduction to programming using examples drawn from animation and graphics. Fundamental programming language features are covered, including variables, expressions, statements, loops, functions, and objects. Class design, event-driven programming or other advanced programming techniques may be introduced as needed. No prior programming experience is assumed. Prerequisite: Recommended: BC Math 12 or equivalent. Students with credit for CMPT 102, 120, 128 or 130 may not take this course for further credit. Students who have taken CMPT 125, 129 or 135 first may not then take this course for further credit. Quantitative/Breadth-Science.

CMPT 201 - Systems Programming (4)

An introduction to a UNIX-like application-OS interface from a programmer's perspective. Introduces operating systems and their interfaces for user-level programs. Students learn how to programmatically interact with an OS efficiently, correctly, and securely. Topics include: command-line tools, programming with memory, processes, threads, IPC, as well as basics of OS security. Prerequisite: (CMPT 125 or CMPT 135) and MACM 101, both with a minimum grade of C-. Students who have taken CMPT 300 first may not then take this course for further credit.

CMPT 210 - Probability and Computing (3)

Probability has become an essential tool in modern computer science with applications in randomized algorithms, computer vision and graphics, systems, data analysis, and machine learning. The course introduces the foundational concepts in probability as required by many modern applications in computing. Prerequisite: MACM 101, MATH 152, CMPT 125 or CMPT 135, and (MATH 240 or MATH 232), all with a minimum grade of C-.

CMPT 213 - Object Oriented Design in Java (3)

An introduction to object oriented design using Java. The Java programming language is introduced, with an emphasis on its advanced features. The course covers the building blocks of object oriented design including inheritance, polymorphism, interfaces and abstract classes. A number of object oriented design patterns are presented, such as observer, iterator, and singleton. The course also teaches best-practices in code construction. It includes a basic introduction to programming event driven graphical user interfaces. Prerequisite: CMPT 225 with a minimum grade of C-. Students with credit for CMPT 212 cannot take this course for further credit.

CMPT 218 - Special Topics in Computing Science (3)

Special topics in computing science which are of current interest or are not covered in the regular curriculum will be offered from time to time depending on availability of faculty and on student interest.

CMPT 225 - Data Structures and Programming (3)

Introduction to a variety of practical and important data structures and methods for implementation and for experimental and analytical evaluation. Topics include: stacks, queues and lists; search trees; hash tables and algorithms; efficient sorting; object-oriented programming; time and space efficiency analysis; and experimental evaluation. Prerequisite: (MACM 101 and (CMPT 125, CMPT 129 or CMPT 135)) or (ENSC 251 and ENSC 252), all with a minimum grade of C-. Quantitative.

CMPT 263 - Introduction to Human-Centered Computing (3)

Surveys contemporary Human-Computer Interaction (HCI) topics on designing, implementing, and evaluating interactive computer systems from a human-centered perspective. Students will learn important concepts including understanding users, incorporating accountability and responsibility in design, creating accessible and inclusive systems, and evaluating system interfaces with well-established methodologies. Prerequisite: CMPT 125 or CMPT 135, with a minimum grade of C-.

CMPT 272 - Web I - Client-side Development (3)

Introduces students to the fundamentals of client-side web programming. Students will gain experience working with markup languages such as HTML and CSS as well as define dynamic behaviour using JavaScript and TypeScript. Students will be introduced to popular front-end frameworks. The web architecture will be discussed, and web services will be invoked using basic API calls to existing REST APIs. Prerequisite: CMPT 120 or CMPT 130 or CMPT 128, with a minimum grade of C-.

CMPT 275 - Software Engineering I (4)

Introduction to software engineering techniques used in analysis/design and in software project management. The course centres on a team project involving requirements gathering, object analysis and simple data normalization, use-case-driven user documentation and design followed by implementation and testing. Additionally, there is an introduction to project planning, metrics, quality assurance, configuration management, and people issues. Prerequisite: One W course, CMPT 225, (MACM 101 or (ENSC 251 and ENSC 252)) and (MATH 151 or MATH 150), all with a minimum grade of C-. MATH 154 or MATH 157 with at least a B+ may be substituted for MATH 151 or MATH 150. Students with credit for CMPT 276 may not take this course for further credit.

CMPT 276 - Introduction to Software Engineering (3)

An overview of various techniques used for software development and software project management. Major tasks and phases in modern software development, including requirements, analysis, documentation, design, implementation, testing,and maintenance. Project management issues are also introduced. Students complete a team project using an iterative development process. Prerequisite: One W course, CMPT 225, (MACM 101 or (ENSC 251 and ENSC 252)) and (MATH 151 or MATH 150), all with a minimum grade of C-. MATH 154 or MATH 157 with at least a B+ may be substituted for MATH 151 or MATH 150. Students with credit for CMPT 275 may not take this course for further credit.

CMPT 295 - Introduction to Computer Systems (4)

The curriculum introduces students to topics in computer architecture that are considered fundamental to an understanding of the digital systems underpinnings of computer systems. Prerequisite: Either (MACM 101 and (CMPT 125 or CMPT 135)) or (MATH 151 and CMPT 102 for students in an Applied Physics program), all with a minimum grade of C-.

CMPT 300 - Operating Systems I (3)

This course aims to give the student an understanding of what a modern operating system is, and the services it provides. It also discusses some basic issues in operating systems and provides solutions. Topics include multiprogramming, process management, memory management, and file systems. Prerequisite: CMPT 225 and (CMPT 295 or ENSC 254), all with a minimum grade of C-.

CMPT 303 - Operating Systems (3)

A course teaching the internals of modern operating systems. Topics consist of kernel components of modern OSes, such as system calls, device drivers, virtual memory, scheduling, file systems, synchronization for multi-core, and memory ordering. Prerequisite: CMPT 225, (CMPT 295 or ENSC 254), and (CMPT 201 or ENSC 351), all with a minimum grade of C-.

CMPT 305 - Computer Simulation and Modelling (3)

An introduction to the modelling, analysis, and computer simulation of complex systems. Topics include analytic modelling, discrete event simulation, experimental design, random number generation, and statistical analysis. Prerequisite: CMPT 225, (MACM 101 or (ENSC 251 and ENSC 252)) and (STAT 270 or STAT 271), all with a minimum grade of C-.

CMPT 307 - Data Structures and Algorithms (3)

Design and analysis of efficient data structures and algorithms. General techniques for building and analyzing algorithms (greedy, divide & conquer, dynamic programming, network flows). Introduction to NP-completeness. Prerequisite: CMPT 225, (MACM 201 or CMPT 210), (MATH 150 or MATH 151), and (MATH 232 or MATH 240), all with a minimum grade of C-. MATH 154 or MATH 157 with a grade of at least B+ may be substituted for MATH 150 or MATH 151.

CMPT 308 - Computability and Complexity (3)

Formal models of computation such as automata and Turing machines. Decidability and undecidability. Recursion Theorem. Connections between computability and logic (Gödel’s Incompleteness). Time and space complexity classes. NP-completeness. Prerequisite: (MACM 201 or CMPT 210) with a minimum grade of C-.

CMPT 310 - Introduction to Artificial Intelligence (3)

A survey of modern approaches for artificial intelligence (AI). Provides an introduction to a variety of AI topics and prepares students for upper-level courses. Topics include: problem solving with search; adversarial game playing; probability and Bayesian networks; machine learning; and applications such as robotics, visual computing and natural language. Prerequisite: CMPT 225 and (MACM 101 or (ENSC 251 and ENSC 252)), all with a minimum grade of C-.

CMPT 318 - Special Topics in Computing Science (3)

Special topics in computing science at the 300 level. Topics that are of current interest or are not covered in regular curriculum will be offered from time to time depending on availability of faculty and student interest. Prerequisite: CMPT 225 with a minimum grade of C-. Additional prerequisites to be determined by the instructor subject to approval by the undergraduate program chair.

CMPT 320 - Social Implications - Computerized Society (3)

An examination of social processes that are being automated and implications for good and evil, that may be entailed in the automation of procedures by which goods and services are allocated. Examination of what are dehumanizing and humanizing parts of systems and how systems can be designed to have a humanizing effect. Prerequisite: A CMPT course with a minimum grade of C- and 45 units. Breadth-Science.

CMPT 340 - Biomedical Computing (3)

The principles involved in using computers for data acquisition, real-time processing, pattern recognition and experimental control in biology and medicine will be developed. The use of large data bases and simulation will be explored. Prerequisite: Completion of 60 units including one of CMPT 125, 126, 128, 135, with a minimum grade of C- or CMPT 102 with a grade of B or higher.

CMPT 353 - Computational Data Science (3)

Basic concepts and programming tools for handling and processing data. Includes data acquisition, cleaning data sources, application of machine learning techniques and data analysis techniques, large-scale computation on a computing cluster. Prerequisite: CMPT 225 and (BUS 232, STAT 201, STAT 203, STAT 205, STAT 270, STAT 271, ENSC 280, MSE 210, or SEE 241), with a minimum grade of C-.

CMPT 354 - Database Systems I (3)

Logical representations of data records. Data models. Studies of some popular file and database systems. Document retrieval. Other related issues such as database administration, data dictionary and security. Prerequisite: CMPT 225 and (MACM 101 or (ENSC 251 and ENSC 252)), all with a minimum grade of C-.

CMPT 361 - Introduction to Visual Computing (3)

Provides a unified introduction to the fundamentals of computer graphics and computer vision (visual computing). Topics include graphics pipelines, sampling and aliasing, geometric transformations, projection and camera models, meshing, texturing, color theory, image filtering and registration, shading and illumination, raytracing, rasterization, animation, optical flow, and game engines. Prerequisite: CMPT 225 and MATH 232 or 240, all with a minimum grade of C-.

CMPT 362 - Mobile Applications Programming and Design (3)

Teaches students how to design and implement smartphone applications. Topics include development environment, phone emulator, key programming paradigms, UI design including views, fragments, and activities, data persistence, threads, services, embedded sensors, and location based services (e.g., Google Maps). Concepts are reinforced through programming assignments and group projects. Prerequisite: CMPT 225 with a minimum grade of C-. Students with credit for IAT 359 may not take this course for further credit.

CMPT 363 - User Interface Design (3)

This course provides a comprehensive study of user interface design. Topics include: goals and principles of UI design (systems engineering and human factors), historical perspective, current paradigms (widget-based, mental model, graphic design, ergonomics, metaphor, constructivist/iterative approach, and visual languages) and their evaluation, existing tools and packages (dialogue models, event-based systems, prototyping), future paradigms, and the social impact of UI. Prerequisite: CMPT 225 and CMPT 263, both with a minimum grade of C-.

CMPT 365 - Multimedia Systems (3)

Multimedia systems design, multimedia hardware and software, issues in effectively representing, processing, and retrieving multimedia data such as text, graphics, sound and music, image and video. Prerequisite: CMPT 225 with a minimum grade of C-.

CMPT 371 - Data Communications and Networking (3)

Data communication fundamentals (data types, rates, and transmission media). Network architectures for local and wide areas. Communications protocols suitable for various architectures. ISO protocols and internetworking. Performance analysis under various loadings and channel error rates. Prerequisite: CMPT 225 and (MATH 151 or MATH 150), with a minimum grade of C-. MATH 154 or MATH 157 with a grade of at least B+ may be substituted for MATH 151 (MATH 150).

CMPT 372 - Web II - Server-side Development (3)

Introduces students to the fundamentals of server-side web development. Students will gain experience working with backend web frameworks, designing and implementing web APIs, and deploying web systems. Students will be introduced to popular back-end frameworks. The course will focus on the design, creating, implementation, and deployment of backend systems, including APIs. Prerequisite: CMPT 272 and CMPT 225, both with a minimum grade of C-. Students with credit for CMPT 470 may not take this course for further credit.

CMPT 373 - Software Development Methods (3)

Survey of modern software development methodology. Several software development process models will be examined, as will the general principles behind such models. Provides experience with different programming paradigms and their advantages and disadvantages during software development. Prerequisite: CMPT 276 or 275, with a minimum grade of C-.

CMPT 376W - Professional Responsibility and Technical Writing (3)

Covers professional writing in computing science, including format conventions and technical reports. The basis for ethical decision-making and the methodology for reaching ethical decisions concerning computing matters will be studied. Students will survey and write research papers, and both individual and group work will be emphasized. Prerequisite: CMPT 105W and (CMPT 275 or CMPT 276), with a minimum grade of C-. Writing.

CMPT 379 - Principles of Compiler Design (3)

This course covers the key components of a compiler for a high level programming language. Topics include lexical analysis, parsing, type checking, code generation and optimization. Students will work in teams to design and implement an actual compiler making use of tools such as lex and yacc. Prerequisite: (MACM 201 or CMPT 210), (CMPT 295 or ENSC 215) and CMPT 225, all with a minimum grade of C-.

CMPT 383 - Comparative Programming Languages (3)

Various concepts and principles underlying the design and use of modern programming languages are considered in the context of procedural, object-oriented, functional and logic programming languages. Topics include data and control structuring constructs, facilities for modularity and data abstraction, polymorphism, syntax, and formal semantics. Prerequisite: CMPT 225 and (MACM 101 or (ENSC 251 and ENSC 252)), all with a minimum grade of C-.

CMPT 384 - Symbolic Computing (3)

This course considers modelling and programming techniques appropriate for symbolic data domains such as mathematical expressions, logical formulas, grammars and programming languages. Topics include recursive and functional programming style, grammar-based data abstraction, simplification and reduction transformations, conversions to canonical form, environment data structures and interpreters, metaprogramming, pattern matching and theorem proving. Prerequisite: CMPT 225 and (MACM 101 or (ENSC 251 and ENSC 252)), all with a minimum grade of C-.

CMPT 400 - 3D Computer Vision (3)

Advanced topics in 3D vision covering topics including acquisition, processing, and synthesis of 3D content. The course introduces 3D representations amenable to computer vision (from classical polygonal meshes to neural fields), and fundamentals of non-linear optimization to effectively tackle inverse 3D vision problems. Prerequisite: CMPT 361 and MACM 316, both with a minimum grade of C-. Recommended: MATH 251. Students who have taken CMPT 469 under the title "3D Computer Vision" offered in Spring 2024 may not take this course for further credit.

CMPT 403 - System Security and Privacy (3)

Starting from cybersecurity principles, students will learn to protect systems from attacks on data confidentiality, integrity, system availability, and user privacy. By modeling system security, students will learn to find weaknesses in software, hardware, networks, data storage systems, and the Internet, and identify current security practices to protect these systems. Prerequisite: CMPT 300 with a minimum grade of C-.

CMPT 404 - Cryptography and Cryptographic Protocols (3)

The main cryptographic tools and primitives, their use in cryptographic applications; security and weaknesses of the current protocols. The notion of security, standard encryption schemes, digital signatures, zero-knowledge, selected other topics. Prerequisite: (MACM 201 or CMPT 210) with a minimum grade of C-. CMPT 307 and 308 are recommended.

CMPT 405 - Design and Analysis of Computing Algorithms (3)

Models of computation, methods of algorithm design; complexity of algorithms; algorithms on graphs, NP-completeness, approximation algorithms, selected topics. Prerequisite: CMPT 307 with a minimum grade of C-.

CMPT 406 - Computational Geometry (3)

Mathematical preliminaries; convex hull algorithms; intersection problems; closest-point problems and their applications. Prerequisite: CMPT 307 with a minimum grade of C-.

CMPT 407 - Computational Complexity (3)

Study of what is, and is not, efficiently computable with limited resources (time, space, randomness, parallelism, nondeterminism, interaction, and quantum). Complexity classes and connections among them. Interplay between complexity and algorithm design. Prerequisite: CMPT 307 with a minimum grade of C-. CMPT 308 is recommended.

CMPT 409 - Special Topics in Theoretical Computing Science (3)

Current topics in theoretical computing science depending on faculty and student interest. Prerequisite: CMPT 307 with a minimum grade of C-.

CMPT 410 - Machine Learning (3)

Machine Learning (ML) is the study of computer algorithms that improve automatically through experience. This course introduces students to the theory and practice of machine learning, and covers mathematical foundations, models such as (generalized) linear models, kernel methods and neural networks, loss functions for classification and regression, and optimization methods. Prerequisite: CMPT 310 and MACM 316, both with a minimum grade of C-. Students with credit for CMPT 419 under the title "Machine Learning" may not take this course for further credit.

CMPT 411 - Knowledge Representation (3)

Formal and foundational issues dealing with the representation of knowledge in artificial intelligence systems are covered. Questions of semantics, incompleteness, non-monotonicity and others will be examined. As well, particular approaches, such as procedural or semantic network, may be discussed. Prerequisite: Completion of nine units in Computing Science upper division courses or, in exceptional cases, permission of the instructor.

CMPT 412 - Computer Vision (3)

Computational approaches to image and video understanding in relation to theories, the operation of the human visual system, and practical application areas such as robotics. Topics include image classification, object detection, image segmentation based mostly on deep neural network and to some extent classical techniques, and 3D reconstruction. Also covers state-of-the-art deep neural architectures for computer vision applications, such as metric learning, generative adversarial networks, and recurrent neural networks. Prerequisite: CMPT 361 and MATH 152, both with a minimum grade of C-.

CMPT 413 - Computational Linguistics (3)

This course examines the theoretical and applied problems of constructing and modelling systems, which aim to extract and represent the meaning of natural language sentences or of whole discourses, but drawing on contributions from the fields of linguistics, cognitive psychology, artificial intelligence and computing science. Prerequisite: Completion of nine units in Computing Science upper division courses or, in exceptional cases, permission of the instructor.

CMPT 415 - Special Research Projects (3)

To be individually arranged. Prerequisite: Permission of Instructor and School.

CMPT 416 - Special Research Projects (3)

To be individually arranged. Prerequisite: Permission of the department.

CMPT 417 - Intelligent Systems (3)

Intelligent Systems using modern constraint programming and heuristic search methods. A survey of this rapidly advancing technology as applied to scheduling, planning, design and configuration. An introduction to constraint programming, heuristic search, constructive (backtrack) search, iterative improvement (local) search, mixed-initiative systems and combinatorial optimization. Prerequisite: CMPT 225 with a minimum grade of C-.

CMPT 419 - Special Topics in Artificial Intelligence (3)

Current topics in artificial intelligence depending on faculty and student interest.

CMPT 420 - Deep Learning (3)

In machine learning, many recent successes have been achieved using neural networks with several layers, so-called deep neural networks. Convolutional neural nets, autoencoders, recurrent neural nets, long-short term memory networks, and generative adversarial networks will be presented. Students will look at techniques for training them from data, and applications. Prerequisite: CMPT 410 or CMPT 419 (Machine Learning), with a minimum grade of C-. Students with credit for CMPT 728 may not take this course for further credit.

CMPT 426 - Practicum I (3)

First term of work experience in the School of Computing Science Co-operative Education Program. Units from this course do not count towards the units required for an SFU degree. Graded as pass/fail (P/F). Prerequisite: Students must complete Bridging Online (visit www.sfu.ca/coop/bol for further details) at least two terms before their anticipated co-op placement. Students must then enroll with the co-op program by the second week of the term preceding the work term. Normally, students will have completed a minimum of 45 units by the end of the term of application, CMPT 275 or 276, and have a minimum CGPA of 2.50.

CMPT 427 - Practicum II (3)

The second term of work experience for students in the Computing Science Co-operative Education Program. Units from this course do not count towards the units required for an SFU degree. Graded as pass/fail (P/F). Prerequisite: CMPT 426, CGPA of 2.50.

CMPT 428 - Practicum III (3)

The third term of work experience for students in the Computing Science Co-operative Education Program. Units from this course do not count towards the units required for an SFU degree. Graded as pass/fail (P/F). Prerequisite: CMPT 427, CGPA of 2.50.

CMPT 429 - Practicum IV (3)

The fourth term of work experience for students in the Computing Science Co-operative Education Program. Units from this course do not count towards the units required for an SFU degree. Graded as pass/fail (P/F). Prerequisite: CMPT 428, CGPA of 2.50.

CMPT 430 - Practicum V (3)

An optional fifth term of work experience for students in the Computing Science Co-operative Education Program. Units from this course do not count towards the units required for an SFU degree. This course may be repeated for credit at most twice. Repeating for credit requires approval of the School. Graded as pass/fail (P/F). Prerequisite: CMPT 429, CGPA of 2.50.

CMPT 431 - Distributed Systems (3)

An introduction to distributed systems: systems consisting of multiple physical components connected over a network. Architectures of such systems, ranging from client-server to peer-to-peer. Distributed systems are analyzed via case studies of real network file systems, replicated systems, sensor networks and peer-to-peer systems. Hands-on experience designing and implementing a complex distributed system. Prerequisite: CMPT 300, 371, both with a minimum grade of C-. Students with credit for CMPT 401 before September 2008 may not take this course for further credit.

CMPT 433 - Embedded Systems (3)

The basics of embedded system organization, hardware-software co-design, and programmable chip technologies are studied. Formal models and specification languages for capturing and analyzing the behavior of embedded systems. The design and use of tools for system partitioning and hardware/software co-design implementation, validation, and verification are also studied. Prerequisite: CMPT 295 and CMPT 300, with a minimum grade of C-.

CMPT 441 - Computational Biology (3)

This course introduces students to the computing science principles underlying computational biology. The emphasis is on the design, analysis and implementation of computational techniques. Possible topics include algorithms for sequence alignment, database searching, gene finding, phylogeny and structure analysis. Prerequisite: CMPT 307 with a minimum grade of C-. Students with credit for CMPT 341 may not take this course for further credit.

CMPT 450 - Computer Architecture (3)

Principles of the architecture of computing systems. Topics include: superscalar processor micro-architecture, speculative execution, cache and memory hierarchy, multiprocessors, cache coherence, memory consistency, implications of technology on architecture, parallel architectures (multi-threading, GPUs, vector processors). Prerequisite: CMPT 295 with a minimum grade of C-.

CMPT 454 - Database Systems II (3)

An advanced course on database systems which covers crash recovery, concurrency control, transaction processing, distributed database systems as the core material and a set of selected topics based on the new developments and research interests, such as object-oriented data models and systems, extended relational systems, deductive database systems, and security and integrity. Prerequisite: CMPT 300 and 354, with a minimum grade of C-.

CMPT 456 - Information Retrieval and Web Search (3)

Introduction to the essentials of information retrieval and the applications of information retrieval in web search and web information systems. Topics include the major models of information retrieval, similarity search, text content search, link structures and web graphics, web mining and applications, crawling, search engines, and some advanced topics such as spam detection, online advertisement, and fraud detection in online auctions. Prerequisite: CMPT 354 with a minimum grade of C-.

CMPT 459 - Special Topics in Database Systems (3)

Current topics in database and information systems depending on faculty and student interest. Prerequisite: CMPT 354 with a minimum grade of C-.

CMPT 461 - Computational Photography and Image Manipulation (3)

Computational photography is concerned with overcoming the limitations of traditional photography with computation: in optics, sensors, and geometry; and even in composition, style, and human interfaces. The course covers computational techniques to improve the way we process, manipulate, and interact with visual media. The covered topics include intrinsic decomposition, monocular depth estimation, edit propagation, camera geometry and optics, computational apertures, advanced image filtering operations, high-dynamic range, image blending, texture synthesis and inpainting. Prerequisite: CMPT 361 with a minimum grade of C-.

CMPT 464 - Geometric Modelling in Computer Graphics (3)

Covers advanced topics in geometric modelling and processing for computer graphics, such as Bezier and B-spline techniques, subdivision curves and surfaces, solid modelling, implicit representation, surface reconstruction, multi-resolution modelling, digital geometry processing (e.g. mesh smoothing, compression, and parameterization), point-based representation, and procedural modelling. Prerequisite: CMPT 361, MACM 316, both with a minimum grade of C-. Students with credit for CMPT 469 between 2003 and 2007 or equivalent may not take this course for further credit.

CMPT 466 - Animation (3)

Topics and techniques in animation, including: The history of animation, computers in animation, traditional animation approaches, and computer animation techniques such as geometric modelling, interpolation, camera controls, kinematics, dynamics, constraint-based animation, realistic motion, temporal aliasing, digital effects and post production. Prerequisite: CMPT 361 and MACM 316, with a minimum grade of C- or permission of the instructor.

CMPT 467 - Visualization (3)

Presents advanced topics in the field of scientific and information visualization. Topics include an introduction to visualization (importance, basic approaches, and existing tools), abstract visualization concepts, human perception, visualization methodology, data representation, 2D and 3D display, interactive visualization, and their use in medical, scientific, and business applications. Prerequisite: CMPT 361, MACM 316, both with a minimum grade of C-.

CMPT 469 - Special Topics in Computer Graphics (3)

Current topics in computer graphics depending on faculty and student interest. Prerequisite: CMPT 361 with a minimum grade of C-.

CMPT 471 - Networking II (3)

This course covers the fundamentals of higher level network functionality such as remote procedure/object calls, name/address resolution, network file systems, network security and high speed connectivity/bridging/switching. Prerequisite: CMPT 300 and 371, with a minimum grade of C-.

CMPT 473 - Software Testing, Reliability and Security (3)

Methods for software quality assurance focusing on reliability and security. Test coverage and test data adequacy including combinatorial testing. MC/DC testing, and mutation testing. Security engineering techniques for vulnerability discovery and mitigation including fuzz testing. Testing techniques will be applied to the assessment of external open source software. Prerequisite: (CMPT 275 or CMPT 276) with a minimum grade of C- and 15 upper division CMPT units.

CMPT 474 - Web Systems Architecture (3)

Web service based systems are fundamentally different from traditional software systems. The conceptual and methodological differences between a standard software development process and the development of a web service based information system. The technology involved during the construction of their own web service based application in an extensive project. Prerequisite: CMPT 371 with a minimum grade of C-.

CMPT 475 - Requirements Engineering (3)

Software succeeds when it is well-matched to its intended purpose. Requirements engineering is the process of discovering that purpose by making requirements explicit and documenting them in a form amenable to analysis, reasoning, and validation, establishing the key attributes of a system prior to its construction. Students will learn methodical approaches to requirements analysis and design specification in early systems development phases, along with best practices and common principles to cope with notoriously changing requirements. Prerequisite: CMPT 275 or CMPT 276, (MACM 201 or CMPT 210) , all with a minimum grade of C- and 15 units of upper division courses. Recommended: Co-op experience.

CMPT 476 - Introduction to Quantum Algorithms (3)

An introductory treatment of quantum computing with an emphasis on quantum algorithms. Topics include the gate model of quantum computation focusing on the design and implementation of quantum algorithms. Basic knowledge of algorithms and complexity will be an asset, but not required. No prior knowledge of physics or quantum mechanics is necessary, only a solid background in linear algebra. Prerequisite: MATH 232 or MATH 240, with a minimum grade of C-. Students who have taken CMPT 409 in Summer 2020 and 2021 under the title "Intro to Quantum Computing" may not take this course for further credit.

CMPT 477 - Introduction to Formal Verification (3)

Introduces, at an accessible level, a formal framework for symbolic model checking, one of the most important verification methods. The techniques are illustrated with examples of verification of reactive systems and communication protocols. Students learn to work with a model checking tool. Prerequisite: CMPT 275 or 276, with a minimum grade of C-.

CMPT 478 - Current Topics in Quantum Computing (3)

Current topics in advanced quantum algorithms and related issues depending on faculty and student interest. Possible topics include the hidden subgroup problem, quantum walk, systems of linear equations, adiabatic quantum computing, quantum system simulation, quantum error correction, quantum circuits and compilation, and quantum machine learning. Prerequisite: CMPT 476 or PHYS 416, with a minimum grade of C-.

CMPT 479 - Special Topics in Computing Systems (3)

Current topics in computing systems depending on faculty and student interest. Prerequisite: CMPT 300 with a minimum grade of C-.

CMPT 489 - Special Topics in Programming Languages (3)

Current topics in programming languages depending on faculty and student interest. Prerequisite: CMPT 383 with a minimum grade of C-.

CMPT 493 - Digital Media Practicum (6)

The design and implementation of interactive digital media systems using modern processes and tools. Projects are provided by external clients or vetted entrepreneurial pitches and are developed by interdisciplinary teams composed of one CMPT 493 student and multiple Master of Digital Media students. Prerequisite: CMPT 275 or CMPT 276, at least 80 units, CGPA and UDGPA over 3.0, enrolled in any CS major program. Participation in the practicum is competitive and an application must be submitted to the Undergraduate Chair by a defined due date announced each term.

CMPT 494 - Software Systems Program Capstone Project I (3)

This course is the first in a series of two 3 unit courses for the Software Systems Capstone Project. Students will work in teams on a closely supervised software systems project. Projects can be research based or have a significant software application, potentially based on a real customer application specification, as their basis. Students will be required to write a full project report and present their project during the Capstone Project Day Presentations. Prerequisite: Students must have completed at least 15 units of upper division CMPT courses. Successful Capstone Project Proposal.

CMPT 495 - Software Systems Capstone Project II (3)

This course is the second in a series of two 3 unit courses for the Software Systems Capstone Project. Students will work in teams or a closely supervised software systems project. Projects can be research based or have a significant software application, potentially based on a real customer specification. Students will be required to write a final project report at the end of the term and do a project presentation during the Capstone Presentation Day. Prerequisite: CMPT 494 with a minimum grade of C-. CMPT 495 must be taken in the term immediately following the successful completion of CMPT 494 and must be for the same project and faculty supervisor.

CMPT 496 - Directed Studies (3)

Independent study in topics selected in consultation with the supervising instructor(s) that are not covered by existing course offerings. Students must submit a proposal to the undergraduate chair, including the name and signature of the supervising faculty member(s). The proposal must include details of the material to be covered and the work to be submitted. Prerequisite: Students must have completed 90 units, including 15 units of upper division CMPT courses, and have a GPA of at least 3.00. The proposal must be submitted to the undergraduate chair at least 15 days in advance of the term. The proposal must be signed by the supervisor(s) and the undergraduate chair.

CMPT 497 - Dual Degree Program Capstone Project (6)

Students will select one project to be completed in their final year of study. Each student must complete a project report and make a project presentation. The project may include: a research survey, a project implementation, a research paper/report. Prerequisite: Submission of a satisfactory capstone project proposal.

CMPT 498 - Honours Research Project (6)

Students must submit a proposal to the Undergraduate Chair, including the name and signature of the supervising faculty member(s). Students must complete a project report and make a project presentation. This course can satisfy the research project requirements for Computing Science honours students. Prerequisite: Students must have completed 90 units, including 15 units of upper division CMPT courses, and have a GPA of at least 3.00. The proposal must be submitted to the Undergraduate Chair at least 15 days in advance of the term. The proposal must be signed by the supervisor(s) and the undergraduate chair.

CMPT 499 - Special Topics in Computer Hardware (3)

Current topics in computer hardware depending on faculty and student interest. Prerequisite: CMPT 250 or ENSC 250, with a minimum grade of C-.

CMPT 626 - Graduate Co-op I (3)

This course is the first term of work experience in the School of Computing Science Co-operative Education Program for graduate students. Units of this course do not count towards computing science breadth requirements. Graded on a satisfactory/unsatisfactory basis. Prerequisite: 12 units of CMPT coursework at the 700-level or higher with a CGPA of at least 3.0. Department Consent is required for enrollment.

CMPT 627 - Graduate Co-op II (3)

This course is the second term of work experience in the School of Computing Science Co-operative Education Program for graduate students. Units of this course do not count towards computing science breadth requirements. Graded on a satisfactory/unsatisfactory basis. Prerequisite: CMPT 626 and a CGPA of at least 3.0. Department Consent is required for enrollment.

CMPT 628 - Graduate Co-op III (3)

This course is the third term of work experience in the School of Computing Science Co-operative Education Program for graduate students. Units of this course do not count towards computing science breadth requirements. Graded on a satisfactory/unsatisfactory basis. Prerequisite: CMPT 627 and a CGPA of at least 3.0. Department Consent is required for enrollment.

CMPT 629 - Graduate Project (3)

Graded on a satisfactory/unsatisfactory basis. Prerequisite: Permission of the Graduate Program Chair.

CMPT 631 - Industrial Internship (3)

An internship in industry or a research environment for graduate research students. A final report will be submitted and graded by the student's supervisor. Units of this course do not count towards computing science breadth requirements. Graded on a satisfactory/unsatisfactory basis. Prerequisite: 12 units of CMPT course work with an SFU CGPA of at least 3.0. Approval of supervisor and a GPC representative is required prior to applying for, or accepting an internship.

CMPT 700 - Technical Writing and Research Communication (3)

Covers technical writing and communication skills for research in computing science. Introduces students to scholarly publication procedures, research collaboration norms, ethics and intellectual property issues, as well as broadly useful tools for writing about and presenting research. Includes research seminar attendance and practicing constructive critique and feedback for ongoing research. The objective is to train well-rounded graduate students, to advance their ability to communicate and review scientific research, to introduce broadly used research software tools, and to become knowledgeable about issues surrounding computing science research.

CMPT 701 - Computability and Logic (3)

Deep connections between logic and computation have been evident since early work in both areas. More recently, logic-based methods have led to important progress in diverse areas of computing science. This course will provide a foundation in logic and computability suitable for students who wish to understand the application of logic in various areas of CS, or as preparation for more advanced study in logic or theoretical CS.

CMPT 705 - Design and Analysis of Algorithms (3)

The objective of this course is to expose students to basic techniques in algorithm design and analysis. Topics will include greedy algorithms, dynamic programming, advanced data structures, network flows, randomized algorithms. Students with credit for CMPT 706 may not take this course for further credit.

CMPT 706 - Design and Analysis of Algorithms for Big Data (3)

Concepts and problem-solving techniques that are used in the design and analysis of efficient algorithms. Special consideration and adaptations for big data applications will be emphasized. Students with credit for CMPT 705 may not take this course for further credit.

CMPT 710 - Computational Complexity (3)

This course provides a broad view of theoretical computing science with an emphasis on complexity theory. Topics will include a review of formal models of computation, language classes, and basic complexity theory; design and analysis of efficient algorithms; survey of structural complexity including complexity hierarchies, NP-completeness, and oracles; approximation techniques for discrete problems. Equivalent Courses: CMPT810.

CMPT 711 - Bioinformatics Algorithms (3)

Fundamental algorithmic techniques used to solve computational problems encountered in molecular biology. This area is usually referred to as Bioinformatics or Computational Biology. Students who have taken CMPT 881 (Bioinformatics) in 2007 or earlier may not take CMPT 711 for further credit.

CMPT 713 - Natural Language Processing (3)

Natural Language Processing is the automatic analysis of human languages such as English, Korean, and thousands of others analyzed by computer algorithms. Unlike artificially created programming languages where the structure and meaning of programs is easy to encode, human languages provide an interesting challenge, both in terms of its analysis and the learning of language from observations. Covers NLP tasks such as language modeling, machine translation, multilingual processing, information extraction, question answering, and other topics relevant to modern NLP. Students with credit for CMPT 825 or CMPT 413 may not take this course for further credit.

CMPT 720 - Robotic Autonomy: Algorithms and Computation (3)

Fundamental concepts in robotics and related fields, including computational methods for solving decision making and algorithms for robots to understand their environment. Topics include modeling and simulation of robotic systems, optimization, optimal control, robotic safety, reinforcement learning, and robotic perception. Applications of the material include unmanned aerial vehicles and self-driving cars.

CMPT 721 - Knowledge Representation and Reasoning (3)

Knowledge representation is the area of Artificial Intelligence concerned with how knowledge can be represented symbolically and manipulated by reasoning programs. This course addresses problems dealing with the design of languages for representing knowledge, the formal interpretation of these languages and the design of computational mechanisms for making inferences. Since much of Artificial Intelligence requires the specification of a large body of domain-specific knowledge, this area lies at the core of AI. Prerequisite: CMPT 310/710 recommended. Cross-listed course with CMPT 411.

CMPT 722 - Rendering and Visual Computing for Artificial Intelligence (3)

Advanced topics in computer graphics with a focus on rendering, and computer graphics for virtual/augmented reality and artificial intelligence systems. Topics include photorealistic rendering; advanced ray tracing; light fields and volume rendering; differentiable and inverse rendering; neural rendering; and high-performance architectures for visual computing systems. Prerequisite: CMPT 361 or equivalent and CMPT 726.

CMPT 724 - Affective Computing (3)

Affective Computing is the study of emotions and their impact in building interactive, intelligent machines and interfaces. Students will learn theories and models of affect (moods, feelings and emotions), as well as computational social signal processing methods to generate socially intelligent behaviour for robots and agents that interact with humans.

CMPT 726 - Machine Learning (3)

Machine Learning is the study of computer algorithms that improve automatically through experience. Provides students who conduct research in machine learning, or use it in their research, with a grounding in both the theoretical justification for, and practical application of, machine learning algorithms. Covers techniques in supervised and unsupervised learning, the graphical model formalism, and algorithms for combining models. Students who have taken CMPT 882 (Machine Learning) in 2007 or earlier may not take CMPT 726 for further credit.

CMPT 727 - Mathematical and Probabilistic Foundations of Machine Learning (3)

Using machine learning algorithms effectively requires understanding their theoretical and conceptual basis. Covers mathematical and probabilistic foundations of machine learning, placing learning methods in a unified framework based on Bayesian reasoning. Students will acquire skills for formulating models, deriving optimization algorithms, and choosing effective approaches for a given learning problem. Topics include parameter estimation, optimization, linear classification and regression, regularization, and probabilistic graphical models.

CMPT 728 - Deep Learning (3)

Machine learning has become the main framework for building programs that perform intelligent tasks. In fields such as computer vision and natural language processing, many recent successes have been achieved using neural nets with several layers, so-called deep neural nets. Students will look at deep neural nets, techniques for training them from data, and significant applications. They will be presented with network architectures such as convolutional neural nets, autoencoders, recurrent neural nets, long-short term memory networks, and generative adversarial networks. Advanced training techniques to be described include dropout, batch normalization, and adaptive step size selection.

CMPT 729 - Reinforcement Learning (3)

Reinforcement learning is the branch of machine learning that studies learning to act. Agents observe, predict, and act to change their environment. Reinforcement learning has notable success in learning to play video & board games, improving robot performance, and task scheduling. Many recent successes have utilized neural nets, an approach known as deep reinforcement learning.

CMPT 732 - Big Data Lab I (6)

The first of two lab courses that are part of the master of science in big data. This lab course aims to provide students with experience needed for a successful career in big data in the information technology industry. Students will earn core concepts of artificial intelligence and data engineering to work with large, or otherwise complex, data sources. Specifically, this includes statistics and data visualization, data pipeline engineering, and modelling. Many of the assignments will be completed on publicly available, massive data sets giving students hands-on experience with cloud computing, streaming data, and scalable computation - algorithms and software tools needed to master programming for big data. Prerequisite: This course is only available to students enrolled in the master of science in big data program.

CMPT 733 - Big Data Lab II (6)

The second of two lab courses that are part of the master of science in big data. This lab course aims to provide students with experience needed for a successful career in big data in the information technology industry. Students will learn core concepts of artificial intelligence and applied data science. Specifically, this includes data analytics, advanced statistics and data visualization, deep learning, and anomaly detection. Many of the assignments will be completed on publicly available, complex data sets giving students experience with algorithms and software tools needed to master programming for big data. Prerequisite: CMPT 732.

CMPT 740 - Database Systems (3)

Introduction to advanced database system concepts, including query processing, transaction processing, distributed and heterogeneous databases, object-oriented and object-relational databases, data mining and data warehousing, spatial and multimedia systems and Internet information systems.

CMPT 741 - Data Mining (3)

The student will learn basic concepts and techniques of data mining. Unlike data management required in traditional database applications, data analysis aims to extract useful patterns, trends and knowledge from raw data for decision support. Such information are implicit in the data and must be mined to be useful.

CMPT 742 - Visual Computing Lab I (6)

Lab practices, combined with instructional offerings, for students to acquire the hands-on experience necessary for a successful career in Visual Computing in the information technology sector. Topics covered will include fundamental and prevalent problems from application domains in the fields of computer graphics, computer vision, human-computer interaction, medical image analysis, as well as visualization. Prerequisite: This course is only available to students enrolled in the master of visual computing program.

CMPT 743 - Visual Computing Lab II (6)

Lab practices, combined with instructional offerings, for students to acquire the hands-on experience necessary for a successful career in Visual Computing in the information technology sector. Topics covered will include fundamental and prevalent problems from application domains in the fields of computer graphics, computer vision, human-computer interaction, medical image analysis, as well as visualization. Prerequisite: CMPT 742. This course is only available to students enrolled in the master of visual computing program.

CMPT 750 - Computer Architecture (3)

Principles of the architecture of computing systems. Topics include: superscaler processor micro-architecture, speculative execution, cache and memory hierarchy, multiprocessors, cache coherence, memory consistency, implications of technology on architecture, parallel architectures (multi-threading, GPUs, vector processors). Prerequisite: Recommended: CMPT 295 or equivalent. Students with credit for CMPT 450 may not take this course for further credit.

CMPT 756 - Distributed and Cloud Systems (3)

Students will learn principles and techniques for processing various data types at real-world scale using distributed and cloud computing resources. Fundamentals of approximation and distributed algorithms will be covered. Handling of large-scale image and video datasets, massive graphs, as well as structured and unstructured text datasets will be studied. Designing and building robust software systems using multicore processors, processor accelerators (e.g., Graphics Processing Units) and cloud resources will be introduced.

CMPT 757 - Frontiers of Visual Computing (3)

A seminar-oriented course covering the latest technological advances and trends in visual computing and relevant domains. The focus is on relating fundamental visual computing concepts and techniques to the inception, evolution, and future prospects of these trend-setting technologies. Prerequisite: This course is only available to students enrolled into the Visual Computing Specialization of the Professional Master's program in Computer Science.

CMPT 762 - Computer Vision (3)

Selected topics in computer vision including cameras, edge detection, feature matching, optical flow, alignment, epipolar geometry, stereo, structure-from-motion, recognition, segmentation, detection, and deep learning.

CMPT 763 - Biomedical Computer Vision (3)

Selected topics in biomedical imaging. Computer visions, medical data and image representation, file formats, segmentation, registration, classification, anatomical shape modeling, machine and deep learning tools and methods.

CMPT 764 - Geometric Modelling in Computer Graphics (3)

Advanced topics in geometric modelling and processing for computer graphics, such as Bezier and B-spline techniques, subdivision curves and surfaces, solid modelling, implicit representation, surface reconstruction, multi-resolution modelling, digital geometry processing (e.g., mesh smoothing, compression, and parameterization), point-based representation, and procedural modelling. Prerequisite: CMPT 361, MACM 316. Students with credit for CMPT 464 or equivalent may not take this course for further credit.

CMPT 766 - Computer Animation and Simulation (3)

Selected topics in computer animation and simulation, including 3D character animation and control, facial animation, simulation of natural phenomena (i.e. fluids, crowd simulation, and deformation of pliant materials).

CMPT 767 - Visualization (3)

Advanced topics in data visualization. Topics covered may include principles of data representation, data presentation, data interaction, data physicalization, data and visualization literacy, data visualization and diversity, open data, and public personal data visualization. Students with credit for CMPT 878 or 775 may not take this course for further credit.

CMPT 769 - Computational Photography and Image Manipulation (3)

Computational Photography is concerned with overcoming the limitations of traditional photography with computation: in optics, sensors, and geometry; and even in composition, style, and human interfaces. The course covers computational techniques to improve the way we process, manipulate, and interact with visual media. The covered topics include image-based lighting and rendering, camera geometry and optics, computational apertures, advanced image filtering operations, high-dynamic range, image blending, texture synthesis and inpainting. Students with credit for CMPT 461 may not take this course for further credit.

CMPT 770 - Parallel and Distributed Computing (3)

Principles involved in designing modern parallel and distributed software systems. The course focuses on covering key concepts like concurrency, synchronization, consistency models and fault tolerance. Involves multiple programming projects and reading articles on recent trends in parallel and distributed computing.

CMPT 771 - Computer Networks (3)

Investigates the design and operation of wide-area computer networks, especially the Internet and the TCP/IP protocol suite. This course studies performance modeling, security and quality of service; wireless connectivity and multimedia networking; network services, including recent topics and trends in these areas.

CMPT 772 - Software Product Engineering and Management (3)

Introduction to modern software engineering with focus to initiate, design, build, test, and deliver software in today's industry.

CMPT 777 - Formal Verification (3)

The goal of formal verification is to prove correctness or to find mistakes in software and other systems. This course introduces, at an accessible level, a formal framework for symbolic model checking, one of the most important verification methods. The techniques are illustrated with examples of verification of reactive systems and communication protocols. Students learn to work with a model checking tool such as NuSMV.

CMPT 780 - Computer Security and Ethics (3)

Cybersecurity involves technology, people, information, and processes to enable assured operations in the existence of vulnerabilities, and adversaries who exploit them. Students will gain insight into the importance and landscape of cybersecurity, understand its career paths, and learn about cyber risk management, network and cloud security, system and software security, and cyber ethics and law.

CMPT 782 - Cybersecurity Lab I (6)

Simulating real attacks on software systems to assess the risk associated with potential security breaches to provide students with hands-on experience necessary for a successful career path in the cybersecurity field. Students are trained as penetration testers to learn how to discover vulnerabilities, exploit vulnerabilities, and to determine what attackers might gain after a successful vulnerability exploitation. Prerequisite: This course is only available to students enrolled in the master of cybersecurity program.

CMPT 783 - Cybersecurity Lab II (6)

Students will learn the fundamental principles of system and network security by studying attacks on computer systems, network and cloud infrastructure and how to prevent and detect them. The focus is on hands-on experiences. Students will be able to explain and reproduce former and recent system attacks, build network defensive systems, and design computer systems that are immune to these attacks. Prerequisite: CMPT 782. This course is only available to students enrolled in the master of cybersecurity program.

CMPT 784 - Cyber Risk Assessment and Management (3)

Cyber risk assessment and management has become a fundamental component of business operations. Understanding risk mitigation is an essential skill for business leaders, thought leaders, analysts, as well as security and technology specialists. This course equips students with a comprehensive understanding of how to identify, manage, estimate, and prioritize cyber risks, threats and vulnerabilities.

CMPT 785 - Secure Software Design (3)

The security of software depends on how well the requirements match the needs that the software is to address, how well the software is designed, implemented, tested, and deployed and maintained. This is an advanced course on the rigorous development and use of software that reliably preserves the security properties of the information and systems it protects.

CMPT 786 - Cloud and Network Security (3)

The course covers network attacks as well as techniques to defend against them. This includes protocol-specific attacks (e.g., TCP/IP and BGP) and generic attacks (e.g., Denial of Service); infrastructure topics such as centralized control, SDN, virtualization, NFV, intrusion detection; and new technologies related to containers, IoT, access, 5G.

CMPT 787 - Ethical Hacking (3)

Development of the structured knowledge base of penetration testing to validate security measures and identify vulnerabilities and providing solutions for tightening system and network security and protecting data from unauthorized access. Provides an understanding of how vulnerable systems can be compromised as a means to motivate how to strengthen the defense.

CMPT 788 - Information Privacy (3)

Technological innovation in how individuals, organizations, and governments collect and share personal information have raised serious concerns. Data breaches have grown in frequency over the past decade, exposing us to identity theft, financial fraud and intellectual property theft. Introduces fundamental privacy concepts in a broad sense with emphasis on challenging and emerging research topics in privacy.

CMPT 789 - Applied Cryptography (3)

Explores modern cryptographic and cryptoanalytics techniques in detail, and emphasizes how such mechanisms can be effectively used within larger security systems, and finding their vulnerabilities. Topics covered include cryptographic primitives, public key encryption, digital signature, message authentication codes, cryptographic protocols, and attacks.

CMPT 800 - 3D Computer Vision (3)

Covers topics including acquisition, processing, and synthesis of 3D content. The course introduces 3D representations amenable to computer vision (from classical polygonal meshes to neural fields), and fundamentals of non-linear optimization to effectively tackle inverse 3D vision problems. Students who have taken CMPT 400 and CMPT 985 under the title "HCI, Visualization, Vision, Multimedia" in Spring 2024 may not take this course for further credit.

CMPT 813 - Computational Geometry (3)

This course covers recent developments in discrete, combinatorial, and algorithmic geometry. Emphasis is placed on both developing general geometric techniques and solving specific problems. Open problems and applications will be discussed.

CMPT 815 - Algorithms of Optimization (3)

This course will cover a variety of optimization models, that naturally arise in the area of management science and operations research, which can be formulated as mathematical programming problems. Equivalent Courses: CMPT860.

CMPT 820 - Multimedia Systems (3)

This seminar course covers current research in the field of multimedia computing. Topics include multimedia data representation, compression, retrieval, network communications and multimedia systems. Computing science graduate student or permission of instructor. Equivalent Courses: CMPT880.

CMPT 822 - Computational Vision (3)

A seminar based on the artificial intelligence approach to vision. Computational vision has the goal of discovering the algorithms and heuristics which allow a two dimensional array of light intensities to be interpreted as a three dimensional scene. By reading and discussing research papers - starting with the original work on the analysis of line drawings, and ending with the most recent work in the field - participants begin to develop a general overview of computational vision, and an understanding of the current research problems.

CMPT 827 - Intelligent Systems (3)

Intelligent systems are knowledge-based computer programs which emulate the reasoning abilities of human experts. This introductory course will analyze the underlying artificial intelligence methodology and survey advances in rule-based systems, constraint solving, incremental reasoning, intelligent backtracking and heuristic local search methods. We will look specifically at research applications in intelligent scheduling, configuration and planning. The course is intended for graduate students with a reasonable background in symbolic programming.

CMPT 828 - Illumination in Images and Video (3)

Explores current research in the field of imaging, computer vision, and smart cameras that aims at identifying, eliminating, and re-lighting the effects of illumination in natural scenes. One salient direction in this research is the identification and elimination of shadows in imagery. The topics touched on in the endeavour include physics-based image understanding, image processing, and information theory. Students in vision and in graphics should be interested in the material in this course.

CMPT 829 - Special Topics in Bioinformatics (3)

Examination of recent literature and problems in bioinformatics. Within the CIHR graduate bioinformatics training program, this course will be offered alternatively as the problem-based learning course and the advanced graduate seminar in bioinformatics (both concurrent with MBB 829). Prerequisite: Permission of the instructor.

CMPT 839 - Advanced Natural Language Processing and Understanding (3)

NLP plays an essential role in many applications, allowing people to communicate with computers through dialog systems, communicate with each other through machine translation systems, and find and process information. The course covers advanced topics in NLP, introducing the state-of-the-art methods for computational understanding, analysis, and generation of natural language text. Prerequisite: CMPT 413 or CMPT 713, CMPT 726 or CMPT 728.

CMPT 843 - Database and Knowledge-base Systems (3)

An advanced course on database systems which focuses on data mining and data warehousing, including their principles, designs, implementations, and applications. It may cover some additional topics on advanced database system concepts, including deductive and object-oriented database systems, spatial and multimedia databases, and database-oriented Web technology.

CMPT 863 - Advanced Topics in Human-Computer Interaction (3)

Advanced topics in human-computer interaction (HCI) will be introduced for better understanding end users, solving a variety of problems in the design of technology, and inventing novel forms of interaction. Focus will be on current trends in HCI research, design of interactive systems, and user-centered evaluation techniques. Students with credit for CMPT 888 or CMPT 985 under the title "Special Topics in Human-Computer Interaction" may not take this course for further credit.

CMPT 886 - Special Topics in Operating Systems (3)

CMPT 889 - Special Topics in Interdisciplinary Computing (3)

CMPT 891 - Advanced Seminar (3)

Acquaints new graduate students with the research interests of the faculty, and introduces students to issues relevant to their graduate students. Grade given: S (satisfactory) or U (unsatisfactory).

CMPT 894 - Directed Reading (3)

CMPT 895 - Master Program Extended Essay (3)

Students will complete an extended essay required by the SFU-ZU graduate dual degree master program. The extended essay will normally be a report on a research/industry project or a survey on a specific topic in information technology. The topic and the scope of each essay will be determined in consultation with the supervisory committee. Graded on a satisfactory/unsatisfactory basis.

CMPT 896 - MSc Course Option Portfolio

Required for students enrolled in the MSc course option. Students may only enroll for this course during the term in which he/she enrolls for his/her 10th course. Graded on a satisfactory/unsatisfactory basis.

CMPT 897 - MSc Project (10)

Graded on a satisfactory/unsatisfactory basis.

CMPT 898 - MSc Thesis (18)

Graded on a satisfactory/unsatisfactory basis.

CMPT 899 - PhD Thesis (18)

Graded on a satisfactory/unsatisfactory basis.

CMPT 980 - Special Topics in Computing Science (3)

This course aims to give students experience to emerging important areas of computing science. Prerequisite: Instructor discretion.

CMPT 981 - Special Topics in Theoretical Computing Science (3)

CMPT 982 - Special Topics in Networks and Systems (3)

CMPT 983 - Special Topics in Artificial Intelligence (3)

CMPT 984 - Special Topics in Databases, Data Mining, Computational Biology (3)

CMPT 985 - Special Topics in Graphics, HCI, Visualization, Vision, Multimedia (3)

Examines current research topics in computer graphics, human computer interaction (including audio), computer vision and visualization.