Statistical learning and modelling
This course will attempt to give an intermediate-level overview of concepts and methods in statistics and statistical modelling. I'll aim to present both mathematical theory including proofs as well as practical details of implementation via description of algorithms and programming.
Lecturer
Matthew Cedric Menzies
Date
19th September ~ 14th December, 2023
Location
| Weekday | Time | Venue | Online | ID | Password |
|---|---|---|---|---|---|
| Tuesday,Thursday | 10:40 - 12:15 | Online | ZOOM 08 | 787 662 9899 | BIMSA |
Prerequisite
Strictly speaking, no prerequisite is necessary other than basic linear algebra and real analysis, but the following could be helpful for motivation: an understanding of measure theory; an introductory course in statistics, covering concepts such as sampling, the normal distribution, etc; an introductory course in statistical learning covering things like linear regression.
Syllabus
Larry Wasserman, All of Statistics: A Concise Course in Statistical Inference
Gareth James et al, An Introduction to Statistical Learning
Trevor Hastie et al, The Elements of Statistical Learning
Gareth James et al, An Introduction to Statistical Learning
Trevor Hastie et al, The Elements of Statistical Learning
Audience
Graduate
Video Public
Yes
Notes Public
Yes
Language
English