Designed for students specializing in mathematics, physics, chemistry, computing science and engineering. Topics as for Math 151 with a more extensive review of functions, their properties and their graphs. Recommended for students with no previous knowledge of Calculus. In addition to regularly scheduled lectures, students enrolled in this course are encouraged to come for assistance to the Calculus Workshop (Burnaby), or Math Open Lab (Surrey). Prerequisite: Pre-Calculus 12 (or equivalent) with a grade of at least B+, or MATH 100 with a grade of at least B-, or achieving a satisfactory grade on the Simon Fraser University Calculus Readiness Test. Students with credit for either MATH 151, 154 or 157 may not take MATH 150 for further credit. Quantitative.

Designed for students specializing in mathematics, physics, chemistry, computing science and engineering. Logarithmic and exponential functions, trigonometric functions, inverse functions. Limits, continuity, and derivatives. Techniques of differentiation, including logarithmic and implicit differentiation. The Mean Value Theorem. Applications of differentiation including extrema, curve sketching, Newton's method. Introduction to modeling with differential equations. Polar coordinates, parametric curves. Prerequisite: Pre-Calculus 12 (or equivalent) with a grade of at least A, or MATH 100 with a grade of at least B, or achieving a satisfactory grade on the Simon Fraser University Calculus Readiness Test. Students with credit for either MATH 150, 154 or 157 may not take MATH 151 for further credit. Quantitative.

Designed for students specializing in the life sciences. Topics include: limits, growth rate and the derivative; elementary functions, optimization and approximation methods, and their applications, integration, and differential equations; mathematical models of biological processes and their implementation and analysis using software. Prerequisite: Pre-Calculus 12 (or equivalent) with a grade of at least B, or MATH 100 with a grade of at least C-, or achieving a satisfactory grade on the Simon Fraser University Calculus Readiness Test. Students with credit for either MATH 150, 151 or 157 may not take MATH 154 for further credit. Quantitative.

Designed for students specializing in business or the social sciences. Topics include: limits, growth rate and the derivative; logarithmic, exponential and trigonometric functions and their application to business, economics, optimization and approximation methods; introduction to functions of several variables with emphasis on partial derivatives and extrema. Prerequisite: Pre-Calculus 12 (or equivalent) with a grade of at least B, or MATH 100 with a grade of at least C, or achieving a satisfactory grade on the Simon Fraser University Calculus Readiness Test. Students with credit for either MATH 150, 151 or 154 may not take MATH 157 for further credit. Quantitative.

Riemann sum, Fundamental Theorem of Calculus, definite, indefinite and improper integrals, approximate integration, integration techniques, applications of integration. First-order separable differential equations and growth models. Sequences and series, series tests, power series, convergence and applications of power series. Prerequisite: MATH 150 or 151, with a minimum grade of C-; or MATH 154 or 157 with a grade of at least B. Students with credit for MATH 155 or 158 may not take this course for further credit. Quantitative.

Designed for students specializing in the life sciences. Topics include: vectors and matrices, partial derivatives, multi-dimensional integrals, systems of differential equations, compartment models, graphs and networks, and their applications to the life sciences; mathematical models of multi-component biological processes and their implementation and analysis using software. Prerequisite: MATH 150, 151 or 154, with a minimum grade of C-; or MATH 157 with a grade of at least B. Students with credit for MATH 152 or 158 may not take this course for further credit. Quantitative.

Designed for students specializing in business or the social sciences. Topics include: theory of integration, integration techniques, applications of integration; functions of several variables with emphasis on double and triple integrals and their applications; introduction to differential equations with emphasis on some special first-order equations and their applications; sequences and series. Prerequisite: MATH 150 or 151 or 154 or 157, with a minimum grade of C-. Students with credit for MATH 152 or 155 may not take MATH 158 for further credit. Quantitative.

Linear equations, matrices, determinants. Introduction to vector spaces and linear transformations and bases. Complex numbers. Eigenvalues and eigenvectors; diagonalization. Inner products and orthogonality; least squares problems. An emphasis on applications involving matrix and vector calculations. Prerequisite: MATH 150 or 151 or MACM 101, with a minimum grade of C-; or MATH 154 or 157, both with a grade of at least B. Students with credit for MATH 240 may not take this course for further credit. Quantitative.

Linear equations, matrices, determinants. Real and abstract vector spaces, subspaces and linear transformations; basis and change of basis. Complex numbers. Eigenvalues and eigenvectors; diagonalization. Inner products and orthogonality; least squares problems. Applications. Subject is presented with an abstract emphasis and includes proofs of the basic theorems. Prerequisite: MATH 150 or 151 or MACM 101, with a minimum grade of C-; or MATH 154 or 157, both with a grade of at least B. Students with credit for MATH 232 cannot take this course for further credit. Quantitative.

An introduction to business statistics (descriptive and inferential statistics) with a heavy emphasis on applications and the use of EXCEL. Students will be required to use statistical applications to solve business problems. Corequisite: MATH 150, MATH 151, MATH 154, or MATH 157, with a minimum grade of C-; 15 units. Students with credit for BUEC 232 or ECON 233 may not take this course for further credit. Quantitative.

Basic laws of probability, sample distributions. Introduction to statistical inference and applications. Prerequisite: or Corequisite: MATH 152 or 155 or 158, with a minimum grade of C-. Students wishing an intuitive appreciation of a broad range of statistical strategies may wish to take STAT 100 first. Quantitative.

This is an introductory course in probability and statistics that is designed for Computer Science students. Mainly covers basic probability theory and statistical methods for designing and analyzing computing algorithms and systems. Topics include continuous probability distributions, random variables, multivariate normal distributions, parameter estimation and inference theory, as well as design and analysis of statistical studies, including hypothesis testing and presentation of statistical data. Prerequisite: CMPT 210 with a minimum grade of C-.

This course provides a basic integrative overview of how cognitive science aspires to integrate the empirical findings, theories, and methods of psychology, neuroscience, linguistics, computing science and philosophy. Prerequisite: Open to all students. Students with credit for COGS 200 may not take COGS 100 for further credit. Breadth-Hum/Social Sci/Science.

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.

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.

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.

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.

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.

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.

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.

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-.

Introduction to graph theory, trees, induction, automata theory, formal reasoning, modular arithmetic. Prerequisite: BC Math 12 (or equivalent), or any of MATH 100, 150, 151, 154, 157. Quantitative/Breadth-Science.

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-.

A continuation of MACM 101. Topics covered include graph theory, trees, inclusion-exclusion, generating functions, recurrence relations, and optimization and matching. Prerequisite: MACM 101 or (ENSC 251 and one of MATH 232 or MATH 240). Quantitative.

Explores how language works. Introduces students to the systematic nature of language by exploring the patterns of sounds, words, sentences and meanings in English and other languages. Develops problem-solving and critical thinking skills through hands-on training in pattern recognition and language data analysis. Open to all students. Breadth-Social Sciences.

Develops skills in language analysis by focusing on reading and writing of linguistic argumentation. Explores the foundations of such argumentation in the core areas of linguistics. Students read and discuss primary literature in linguistics in order to understand how to formulate hypotheses and evaluate them. They also learn how to use writing to construct their own solutions to challenging linguistic problems. Prerequisite: LING 220. Writing/Quantitative.

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-.

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.

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-.

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.

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-.

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.

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.

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.

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.

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-.

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

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.

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-.

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-.

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-.

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-.

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-.

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.

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.

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-.

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

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-.

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-.

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).

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-.

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-.

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.

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-.

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-.

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-.

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

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

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 (STAT 101, STAT 270, STAT 271, ENSC 280, or MSE 210), with a minimum grade of C-.

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-.

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.

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.

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.

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-.

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-.

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

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-.

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-.

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-.

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-.

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.

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.

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-.

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

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.

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-.

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.

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-.

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

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.

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

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.

Languages, grammars, automata and their applications to natural and formal language processing. Prerequisite: MACM 201. Quantitative.

An overview of theoretical principles in phonology. Prerequisite: LING 282W.

Introduces theories of sentence structure. Prerequisite: LING 282W.

Formal systems and their relation to linguistic methods and theory. Topics include the mathematical properties of natural languages, and rigorously defined frameworks for linguistic analysis and their formal properties. Prerequisite: LING 322. Recommended: PHIL 210. Quantitative.

Introduction to theoretical and applied issues in the computational processing of natural language. Prerequisite: LING 250 or SDA 250.

Word structure in natural languages and its relationship to phonological and syntactic levels of grammar. Prerequisite: LING 282W.

Basic formal aspects of meaning (e.g. compositional semantics, truth conditional semantics and quantification in natural language) and how they are distinguished from pragmatic aspects of meaning. Prerequisite: LING 282W. Quantitative.

A survey of methods of speech sound description and transcription. Prerequisite: LING 282W.

Advanced training in speech sound description and analysis in the impressionistic and instrumental modes. Prerequisite: LING 330.

Investigation of a selected area of linguistic research. This course may be repeated once for credit if the topic is different. Prerequisite: Requirements will vary according to the topic offered.

Investigation of a selected area of linguistic research. This course may be repeated once for credit if the subject is different. Prerequisite: Requirements will vary according to the topic offered.