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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 406 101 Lecture 1 Tue Thu 8:00 9:30

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 one month after registration opens if not filled.

  STAT 406 L1A Laboratory 1 Mon 9:00 10:00

Labs begin the second week of classes.

Full STAT 406 L1B Laboratory 1 Thu 13:00 14:00

Labs begin the second week of classes.

  STAT 406 L1C Laboratory 1 Wed 8:00 9:00
  STAT 406 L1D Laboratory 1 Fri 8:00 9:00