Courses

Students are required to choose a topic for their senior group project (CMPT 420 or 421) in consultation with the instructor. Selected readings and references pertinent to the topic are assigned. A final written report (software requirements document) is produced giving a detailed specification of the proposed software project.

Students work on a major collaborative software project in a chosen area of computing science and provide a final report and presentation. At least two, and normally not more than five people, work as a team to design, code, debug, test, and document the software.

This course introduces theories, techniques, and applications of immersive computing and related technologies. Students will be introduced to advanced 3D modeling/animation and virtual environment building techniques. In a semester-long project, students will develop their projects using an immersive framework to design, build, and evaluate immersive virtual environments.

An overview of the interdisciplinary science of genomics, proteomics, and bioinformatics which applies the tools of information technology (computer hardware and software) to analyze biological data such as gene or protein sequences. This course examines the theory of bioinformatics as well as its practical application to biological problems using approaches such as BLAST searches, phylogenetics, and protein structure function analysis.

This course addresses functions, limits and continuity, derivatives and applications, and integrals and applications.

Transcendental functions, integration techniques, polar co-ordinates, sequences, series, and Taylor series.

Multivariate calculus. Topics include vectors, vector functions and derivatives; curves; partial and directional derivatives; Lagrange multipliers; double and triple integrals; spherical and cylindrical co-ordinates; vector integrals, Green's Theorem, and surface integrals.

Systems of linear equations, matrices, determinants, vector spaces, linear transformations, eigenvalues and eigenvectors, diagonalization applications, and linear programming.

Foundational mathematical concepts underpinning theoretical frameworks in data science that depend on linear algebra and multivariable calculus, with applications chosen from machine learning, statistical inference, and data assimilation. Possible topics include matrix decompositions, gradient and multivariate chain rule, Lagrange multipliers and constrained optimization, maximum likelihood, and Bayesian estimation.