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STAT 4601 [0.5 credit] Data Mining I (Honours)

Data visualization; knowledge discovery in datasets; unsupervised learning: clustering algorithms; dimension reduction; supervised learning: pattern recognition, smoothing techniques, classification. Computer software will be used.
Includes: Experiential Learning Activity
Prerequisite(s): STAT 3553 or STAT 3503 or MATH 3806, or permission of the School.
Lectures three hours a week, laboratory one hour a week.