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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.
- Choose one section from all 2 activity types. (e.g. Lecture and Laboratory)
| Status | Section | Activity | Term | Interval | Days | Start Time | End Time | Comments |
|---|---|---|---|---|---|---|---|---|
| Full | STAT 406 101 | Web-Oriented Course | 1 | Tue Thu | 15:30 | 17:00 | ||
| STAT 406 WL1 | Waiting List | 1 | Tue Thu | 9:30 | 11:00 | Before registering on the wait list, please read the Registration and wait list FAQ page: https://www.stat.ubc.ca/undergrad-registration-info-and-faqs Please note that registration on the wait list does not guarantee a seat in the course |
Section Comments
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: https://www.stat.ubc.ca/undergrad-registration-info-and-faqs
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