Machine Learning is the study of computer algorithms that improve automatically through experience. Provides students who conduct research in machine learning, or use it in their research, with a grounding in both the theoretical justification for, and practical application of, machine learning algorithms. Covers techniques in supervised and unsupervised learning, the graphical model formalism, and algorithms for combining models. Students who have taken CMPT 882 (Machine Learning) in 2007 or earlier may not take CMPT 726 for further credit.

Lab practices, combined with instructional offerings, for students to acquire the hands-on experience necessary for a successful career in Visual Computing in the information technology sector. Topics covered will include fundamental and prevalent problems from application domains in the fields of computer graphics, computer vision, human-computer interaction, medical image analysis, as well as visualization. Prerequisite: This course is only available to students enrolled in the master of visual computing program.

Lab practices, combined with instructional offerings, for students to acquire the hands-on experience necessary for a successful career in Visual Computing in the information technology sector. Topics covered will include fundamental and prevalent problems from application domains in the fields of computer graphics, computer vision, human-computer interaction, medical image analysis, as well as visualization. Prerequisite: CMPT 742. This course is only available to students enrolled in the master of visual computing program.

Students will learn principles and techniques for processing various data types at real-world scale using distributed and cloud computing resources. Fundamentals of approximation and distributed algorithms will be covered. Handling of large-scale image and video datasets, massive graphs, as well as structured and unstructured text datasets will be studied. Designing and building robust software systems using multicore processors, processor accelerators (e.g., Graphics Processing Units) and cloud resources will be introduced.

This course is the first term of work experience in the School of Computing Science Co-operative Education Program for graduate students. Units of this course do not count towards computing science breadth requirements. Graded on a satisfactory/unsatisfactory basis. Prerequisite: 12 units of CMPT coursework at the 700-level or higher with a CGPA of at least 3.0. Department Consent is required for enrollment.

Graded on a satisfactory/unsatisfactory basis. Prerequisite: Permission of the Graduate Program Chair.