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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
  STAT 406 L1A Laboratory 1 Tue 13:00 14:00

Labs begin the second week of classes.

  STAT 406 L1B Laboratory 1 Fri 15:00 16:00

Labs begin the second week of classes.

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