Course code:
STAT*6801
Section:
01
Attachments:
| Attachment | Size |
|---|---|
 Course Outline (Fall 2015) [1] | 55.52 KB |
| Course Outline (Fall 2016) [2] | 94.13 KB |
| Course Outline (Fall 2017) [3] | 144.85 KB |
| Course Outline (Fall 2018) [4] | 95.08 KB |
| Course Outline (Fall 2019) [5] | 81.2 KB |
| Course Outline (Fall 2020) [6] | 610.09 KB |
| Course Outline (Fall 2021) [7] | 239.57 KB |
| Course Outline (Fall 2022) [8] | 240.35 KB |
| Course Outline (Fall 2023) [9] | 880.97 KB |
Subject:
Statistics [10]
Course name:
Statistical Learning
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.
Course term:
Fall [11]
Course level:
6000 [12]
Academic level:
Graduate [13]
