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Courses

A summary of each course to help with your selection.

Academic Year

Programs

Course ID

Course ID	Course
CMPT 340	CMPT 340 Discrete Structures and Computing Course Credits: 3 This is a second course in the topics of pure mathematics, particularly those most commonly used in the study of computing science and related applications. It includes proof techniques, models of computation, formal languages, analysis of algorithms, trees and advanced general graph theory with applications, finite state and automata theory, encryption, and an elementary introduction to mathematical structures such as groups, rings, and fields. Cross-listed: MATH 340 Prerequisite(s): CMPT 150 or MATH 150. NB: Not offered every year. See department chair
CMPT 345	CMPT 345 Simulation and Modeling Course Credits: 3 This course is designed to give students the ability to analyze, formulate, and program problems related to discrete simulation methods. The course introduces students to queuing theory and some commonly used continuous and discrete statistical distributions. By the end of the course, students are able to simulate real world computer systems and industrial manufacturing systems. Prerequisite(s): CMPT 166 and 231.
CMPT 360	CMPT 360 Comparative Programming Languages Course Credits: 3 The history, development, and design principles for programming notations. The design and internal operations of the major notational categories are examined in detail. Students are expected to become proficient in at least four languages they have not previously learned, typically chosen from historical, modern working, and cutting edge languages and from among such (non-exclusive) categories as Algol-descended, functional, scripting, Web-based, modular, application-specific, visual, and object oriented. They will also learn how to select appropriate programming notations for a given project. Programming will be undertaken in at least three OS environments. Prerequisite(s): CMPT 140, 166 and 231. (3-0) NB: Not offered every year. See department chair
CMPT 370	CMPT 370 Computer Graphics Course Credits: 3 This course introduces the fundamentals of computer graphics and principles of raster image generation. Topics include: graphics primitives, coordinate systems, transformations, rendering techniques, and geometric modelling. Prerequisite(s): CMPT 150, 166, 231; MATH 250. (3-0) NB: Not offered every year. See department chair
CMPT 375	CMPT 375 Human-Computer Interaction Design Course Credits: 3 This course provides a general introduction to interaction design from a human-computer interaction perspective. Students will learn both theoretical and practical concepts of human-computer interaction which will help them discover requirements, design/prototype and evaluate interactive products with usability and user experience (UX) goals. The course covers human capabilities, design principles, prototyping techniques, implementation, and evaluation techniques for interactive products. Students will apply what they learn from lectures to actual challenges of interactive product design, prototyping, implementation, and evaluation. Prerequisite(s): CMPT 166, 231 (3,0)
CMPT 380	CMPT 380 Artificial Intelligence Course Credits: 3 Artificial Intelligence: knowledge representation, logic programming, knowledge inference. Application domains within the discipline of Artificial Intelligence include logical and probabilistic reasoning, natural language understanding, vision and expert systems. Prerequisite(s): CMPT 150, 166, 231 (3-0) NB: Not offered every year. See department chair
CMPT 382	CMPT 382 Machine Learning Course Credits: 3 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. Topics include identification and extraction of useful features that best represent available data, some of the most important machine learning algorithms, and the evaluation of algorithm performance. Prerequisite(s): CMPT 166, 231; or with permission of instructor. (3-0)
CMPT 385	CMPT 385 Introduction to Software Engineering Course Credits: 3 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. Prerequisite(s): CMPT 150, 166, and 231. (3-0) NB: Not offered every year. See department chair
CMPT 386	CMPT 386 Software Engineering II Course Credits: 3 This course will study advanced techniques, tools, and standards in software engineering. The student will complete a semester-long team based project Prerequisite(s): CMPT 385. (3-0) NB: Not offered every year.

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