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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 |
|---|---|---|---|---|---|---|---|---|
| STAT 406 101 | Lecture | 1 | Tue Thu | 8:00 | 9:30 | |||
| STAT 406 L1A | Laboratory | 1 | Mon | 9:00 | 10:00 | Labs begin the second week of classes. | ||
| 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 | |||
| Full | STAT 406 L1D | Laboratory | 1 | Fri | 15:00 | 16:00 |
Section Comments
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 1ST if not filled.