Statistical Learning (STAT*6801)
Code and section: STAT*6801*01
Term: Fall
Details
Topics include: nonparametric and semiparametric regression; kernel methods; regression splines; local polynomial models; generalized additive models; classification and regression trees; neural networks. This course deals with both the methodology and its application with appropriate software. Areas of application include biology, economics, engineering and medicine.
Syllabus
| Attachment | Size |
|---|---|
 Course Outline (Fall 2015) | 55.52 KB |
| Course Outline (Fall 2016) | 94.13 KB |
| Course Outline (Fall 2017) | 144.85 KB |
| Course Outline (Fall 2018) | 95.08 KB |
| Course Outline (Fall 2019) | 81.2 KB |
| Course Outline (Fall 2020) | 610.09 KB |
| Course Outline (Fall 2021) | 239.57 KB |
| Course Outline (Fall 2022) | 240.35 KB |
| Course Outline (Fall 2023) | 880.97 KB |