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 JSON [json data] [notebook12]
Wed. 13. JSON and API Examples [http] [notebook13]
2023-04-03 Mon. 14. API Iteration Examples [notebook14]
Wed. 15. XML and XPath [notebook15]
2023-04-10 Mon. 16. Building a Corpus
Wed. [no class]
2023-04-17 Mon. [Workshop]
Wed. [Project 4]; [Self-Eval]