DSST 389: Statistical Learning • Spring 2023 • Taylor Arnold
[⇐]
[syllabus]
[me]
[zoom]
[materials.zip]
[groups]
[class form]
Reading
Slides
Solutions
2023-01-09
Mon.
[introduction]
Wed.
01. Machine Learning I
[machine learning I]
[handout]
[handout01]
2023-01-16
Mon.
[MLK Day]
Wed.
02. Machine Learning II
[machine learning II]
[handout]
[handout02]
2023-01-23
Mon.
03. Text Analysis I
[notebook03]
Wed.
04. Text Analysis II
[notebook04]
2023-01-30
Mon.
[Workshop]
Wed.
[Project 1]
2023-02-06
Mon.
05. Local Models: KNN
[knn]
[notebook05]
Wed.
06. Local Models: GBM
[gbm]
[notebook06]
2023-02-13
Mon.
[Day of Data]
Wed.
07. G-Scores
[gscore]
2023-02-20
Mon.
[Workshop]
Wed.
[Project 2]
2023-02-27
Mon.
08. Unsupervised Learning I
[dimred]
[notebook08]
Wed.
09. Unsupervised Learning II
[notebook09]
2023-03-06
Mon.
[Spring Break]
Wed.
[Spring Break]
2023-03-13
Mon.
10. Exploratory Analysis
Wed.
11. Topic Models
[lda]
[notebook11]
2023-03-20
Mon.
[Workshop;
NBA shots
]
Wed.
[Project 3]
2023-03-27
Mon.
12. APIs and Text
[http]
[notebook12]
Wed.
13. JSON and API Examples
[json]
[notebook13]
2023-04-03
Mon.
14. Wikimedia API
[notebook14]
Wed.
15. Images I
2023-04-10
Mon.
16. Images II
Wed.
[no class]
2023-04-17
Mon.
[Project 4]
Wed.
[Self-Eval]
[Conclusions]
[Template.Rmd]