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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 L1A | Laboratory | 1 | Mon | 9:00 | 10:00 | |||
| 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
Labs begin the second week of classes.