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.

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 research a chosen area of computing science and provide a final written report.

Students research a chosen area of computing science and provide a final written report.

Students are required to choose a topic for their senior thesis (CMPT 410 or 411) in consultation with an instructor. Selected readings and references pertinent to the topic are assigned. A final written report is presented, consisting of a detailed thesis proposal and a literature review.

A study of special topics or issues in computing science that are not considered in-depth in other courses.

Students are required to produce an outline of the topic studied in consultation with the instructor. A course of reading and/or experimentation is pursued according to the approved outline. Assessment may be via examination and/or a final written report.

This course will study advanced techniques, tools, and standards in software engineering. The student will complete a semester-long team based project

An introduction to the theory of designing and carrying out large software projects. All stages of the software engineering cycle, including requirement analysis, design, implementation, testing, and maintenance will be examined. The student will complete a semester-long team based project.

An overview of core machine learning technologies with motivating applications from a variety of disciplines and real‐world data sets. Students will learn how to implement, evaluate, and improve machine learning algorithms. While studying best practice in machine learning, students are introduced to data mining and statistical pattern recognition and learn how to build automatic analytical models.