| Lecture 0 |
June 3 |
Welcome to SURE 2024 |
slides |
| Lecture 1 |
June 4 |
Exploring data: into the tidyverse |
slides |
| Lecture 2 |
June 5 |
Data visualization: the grammar of graphics and ggplot2 |
slides |
| Lecture 3 |
June 6 |
Data visualization: categorical data |
slides |
| Lecture 4 |
June 7 |
Data visualization: quantitative data |
slides |
| Lecture 5 |
June 10 |
Unsupervised learning: \(k\)-means clustering |
slides |
| Lecture 6 |
June 11 |
Unsupervised learning: hierarchical clustering |
slides |
| Lecture 7 |
June 12 |
Data visualization: density estimation |
slides |
| Lecture 8 |
June 13 |
Simulation |
slides |
| Lecture 9 |
June 14 |
Presentations |
slides |
| Lecture 10 |
June 17 |
Supervised learning: the tradeoffs |
slides |
| Lecture 11 |
June 21 |
Supervised learning: linear regression |
slides |
| Lecture 12 |
June 24 |
Supervised learning: variable selection |
slides |
| Lecture 13 |
June 25 |
Supervised learning: regularization |
slides |
| Lecture 14 |
June 26 |
Supervised learning: logistic regression |
slides |
| Lecture 15 |
June 26 |
Supervised learning: generalized linear models |
slides |
| Lecture 16 |
June 27 |
Unsupervised learning: principal component analysis |
slides |
| Lecture 17 |
July 1 |
Unsupervised learning: Gaussian mixture models |
slides |
| Lecture 18 |
July 2 |
Supervised learning: nonparametric regression |
slides |
| Lecture 19 |
July 8 |
Supervised learning: decision trees |
slides |
| Lecture 20 |
July 9 |
Supervised learning: random forests and boosting |
slides |
| Lecture 21 |
July 10 |
Supervised learning: multinomial classification |
slides |
|