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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 101Web-Oriented Course1 Tue Thu15:3017:00

As the course is on-line this term you DO NOT HAVE TO REGISTER FOR LABS!

Registration and wait list information can be found here:

Please note that prerequisites are as stated in the UBC calendar. Registration will not be permited if lacking the stated prerequisite.

Seats reserved for Statistics specializations will be released on AUGUST 4th if not filled.

There will likely be a "mirrored" online class in the early evening as an alternative to the 3.30pm session

BlockedSTAT 406 WL1Waiting List1 Tue Thu9:3011:00

Before registering on the wait list, please read the Registration and wait list FAQ page:

Please note that registration on the wait list does not guarantee a seat in the course