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.

A geographical introduction to how humans shape our world, with attention also given to how it shapes us. Themes may include: culture, economic activities, environmental change, globalization, politics, population, resources, and urbanization. Breadth-Humanities/Social Sciences.

An introduction to landforms, climates, soils and vegetation; their origins, distributions, interrelationships and roles in the ecosystem. Laboratory work and field trips are included. Breadth-Science.

An introduction to the theories and practices of remote sensing, including sensors and platforms, image collection, preliminary image analysis and interpretation, and a review of remote sensing applications in environmental monitoring and resource management. Quantitative/Breadth-Science.

A basic overview of Geographical Information Systems and Science; GIS software, hardware, data structures and models; spatial data, operations and algorithms; practical applications and limitations. Students with credit for GEOG 354 may not take this course for further credit. Quantitative.

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.

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.

An exploration of the processes that shape Earth's surface and the landforms that result. Prerequisite: GEOG 111 or EASC 101. Quantitative/Breadth-Science.

An examination of the basic principles and processes governing the Earth's weather and climate. Topics include: radiation, greenhouse effect, clouds, precipitation, atmospheric circulation, mid-latitude cyclones, tropical storms, climate change. Prerequisite: GEOG 111. Quantitative.

An introduction to the planetary biosphere, its living organisms, and their interactions with each other and the Earth system. Prerequisite: GEOG 111.

The fundamentals of economics geography, the study of the forces that shape the arrangement of economic activity in the real world. Prerequisite: GEOG 100. Breadth-Social Sciences.

An introduction to key concepts and contexts in contemporary geographical approaches to social practices, meanings, and struggles. Prerequisite: One of GEOG 100, INDG 101, SA 101, or SA 150. Breadth-Social Sciences.

An introduction to key concepts and themes in contemporary geographical approaches to cities and urbanization. Prerequisite: GEOG 100 or 102. Breadth-Social Sciences.

An introduction to basic quantitative techniques for the collection of geographic data. Topics include describing data, gathering samples, theoretical distributions, linking samples and populations, testing significance, and exploring spatial relationships all within practical, real-world application contexts. 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-.

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.

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.

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-.

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 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-.

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-.

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 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-.

Elements of cartographic analysis, design and visualization, with an emphasis on digital mapping, animation techniques, cartographic software and internet mapping. Prerequisite: GEOG 255. Quantitative.

Advanced quantitative techniques for spatial analysis of geographic data and patterns. Topics include geostatistics, spatial interpolation, autocorrelation, kriging, and their use in geographic problem solving with spatial analysis software. Prerequisite: GEOG 251 or one of STAT 201, 203 (formerly 103), 205, or 270. Quantitative.

Advanced remote sensing principles and data processing techniques, including image correction and enhancement, advanced image analysis and information extraction, land cover classification and change detection, and integration of remote sensing and GIS. Prerequisite: GEOG 253. Quantitative.

An examination of technical components of GIS. Topics include spatial representations, generalization and data management; computational algebra and set theory; digital surfaces and terrain models. Prerequisite: GEOG 255. Quantitative.

Introduction to 3D spatial data, 3D analysis, and 3D visualization for spatial problems. Students will gain skills in 3D aspects of GIScience concepts; data generation and use; analysis and simulation; visualization and its use for interpretation and communication. Prerequisite: GEOG 255.

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.

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-.

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

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-.

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-.

Spatial models for the representation and simulation of physical, human and environmental processes. GIS and spatial analysis software are used in the laboratory for model development, from problem definition and solution to visualization. Prerequisite: GEOG 251 or one of STAT 201, 203 (formerly 103), 205, or 270; one of GEOG 351, 352, 353, 355 or 356. Quantitative.

Students will work in teams on real-world remote sensing projects in their area of interest. Each team will complete the project independently from literature review to project presentation. Cutting-edge remote sensing technologies and research that are related to the projects will also be introduced. Prerequisite: GEOG 353. Recommended: One of GEOG 351, 352, 355 or 356. Students with credit for GEOG 453W may not repeat this course for further credit. Quantitative.

A critical examination of advanced topics in GIS, such as: boundary definition, expert systems and artificial intelligence, error and uncertainty, and scale in a digital context. Examines social applications and the roles of GIS in society. Students will design original projects, including data acquisition, analysis, and web site development. Prerequisite: GEOG 355. Students with credit for GEOG 452 or GEOG 455W may not take this course for further credit. Quantitative.

A critical examination of advanced topics in GIS, such as: boundary definition, expert systems and artificial intelligence, error and uncertainty, and scale in a digital context. Examines social applications and the roles of GIS in society. Students will design original projects, including data acquisition, analysis, and web site development. Prerequisite: GEOG 355. Students with credit for GEOG 452 or GEOG 455 may not take this course for further credit. Writing/Quantitative.

The concepts, theories, and technology behind interactive and immersive interface technologies used for geospatial visualization. Applications and implications for GIScience and spatial knowledge acquisition. Combines GIScience, spatial cognition, and virtual environments/interface research perspectives. Prerequisite: GEOG 356. Students with credit for GEOG 457 (STT) Geospatial Virtual Environments in fall 2005 or fall 2006 may not take this course for further credit.