Students explore various multivariate statistical techniques to handle large datasets. Students learn various methods of dimension reduction and feature selection including PCA, CCA, SVD, and factor analysis. Students learn how to manipulate a variety of established learning algorithms such as k-Means clustering and hierarchical clustering. Students also learn some classic supervised techniques such as discriminant analysis and classification trees which extend to random forests. Students learn about boosting and bagging to improve prediction. Prerequisite: ADSC 2020 (min. grade of C)
Students explore various multivariate statistical techniques to handle large datasets. Students learn various methods of dimension reduction and feature selection including PCA, CCA, SVD, and factor analysis. Students learn how to manipulate a variety of established learning algorithms such as k-Means clustering and hierarchical clustering. Students also learn some classic supervised techniques such as discriminant analysis and classification trees which extend to random forests. Students learn about boosting and bagging to improve prediction. Prerequisite: ADSC 2020 (min. grade of C)
