Introduction to supervised and unsupervised machine learning (ML), including deeper knowledge of several algorithms of each type. Evaluation and quantification of predictive performance of ML systems. Use of one or more ML development environments. Precludes additional credit for COMP 3105, COMP 4105 (no longer offered). Prerequisite(s): (ECOR 2050 or STAT 3502 or STAT 2605 or SYSC 2510), (SYSC 1006 or SYSC 2006) with a minimum grade of C-, and third-year status in Engineering. Lectures three hours a week, problem analysis one hour a week.
Introduction to supervised and unsupervised machine learning (ML), including deeper knowledge of several algorithms of each type. Evaluation and quantification of predictive performance of ML systems. Use of one or more ML development environments. Precludes additional credit for COMP 3105, COMP 4105 (no longer offered). Prerequisite(s): (ECOR 2050 or STAT 3502 or STAT 2605 or SYSC 2510), (SYSC 1006 or SYSC 2006) with a minimum grade of C-, and third-year status in Engineering. Lectures three hours a week, problem analysis one hour a week.
