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STAT 406 Methods for Statistical Learning

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.

This course is eligible for Credit/D/Fail grading. To determine whether you can take this course for Credit/D/Fail grading, visit the Credit/D/Fail website. You must register in the course before you can select the Credit/D/Fail grading option.

Credits: 3

Pre-reqs: One of STAT 306, CPSC 340.

Status Section Activity Term Interval Days Start Time End Time Comments
STAT 406 L1ALaboratory1 Mon9:0010:00

Labs begin the second week of classes.

STAT 406 L1BLaboratory1 Thu13:0014:00

Labs begin the second week of classes.

STAT 406 L1CLaboratory1 Wed8:009:00
FullSTAT 406 L1DLaboratory1 Fri15:0016:00