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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 9:30 11:00

Formerly Stat 447B. Students may NOT get credit for Stat 447B and Stat 406. If you have taken Stat 447B, please do not register in this course.

  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.