Flexible, data-adaptive methods for regression and classification models; regression smoothers; penalty methods; assessing accuracy of prediction; model selection; robustness; classification and regression trees; nearest-neighbour methods; neural networks; model averaging and ensembles; computational time and visualization for large data sets. [3-0-1] Prerequisite: a) One of STAT_V 306, CPSC_V 340, or b) STAT_V 301 and one of MATH_V 152, MATH_V 221, MATH_V 223 and one of MATH_V 302, STAT_V 302.
Flexible, data-adaptive methods for regression and classification models; regression smoothers; penalty methods; assessing accuracy of prediction; model selection; robustness; classification and regression trees; nearest-neighbour methods; neural networks; model averaging and ensembles; computational time and visualization for large data sets. [3-0-1] Prerequisite: a) One of STAT_V 306, CPSC_V 340, or b) STAT_V 301 and one of MATH_V 152, MATH_V 221, MATH_V 223 and one of MATH_V 302, STAT_V 302.
