Dates offered
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Lecture(s)
| Time | Mon | Tue | Wed | Thu | Fri |
|---|---|---|---|---|---|
| 08:00 | STAT 406 (Sec. 101) 08:00 to 09:30 |
STAT 406 (Sec. 101) 08:00 to 09:30 |
Labs, Tutorials, etc...
| Time | Mon | Tue | Wed | Thu | Fri |
|---|---|---|---|---|---|
| 08:00 | STAT 406 (Sec. L1C) 08:00 to 09:00 |
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| 09:00 | STAT 406 (Sec. L1A) 09:00 to 10:00 |
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| 14:00 | STAT 406 (Sec. L1B) 14:00 to 15:00 |
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| 15:00 | STAT 406 (Sec. L1D) 15:00 to 16:00 |
Course Term
1
Course Description
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.
Dates offered
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