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. [0.5 credits]
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. [0.5 credits]
