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| Week: date | Topic(s) | Course | Readings | Additional information |
|---|---|---|---|---|
| 1a: 1/19 | Intro to Applied Analytics -- ML pipeline | Slides | None | Bring your laptop to class with R and Rstudio installed |
| 1b: 1/19 | Review of R; manipulating data; using tidyverse | brief R review | None | |
| 2: 1/26 | Decision trees; logistic regression; evaluating machine learning models | Slides | ISL 4.3, 4.6, 8, 2.2 | HW 1 to be released |
| 3: 2/2 | Evaluating prediction; missing data (MCAR/MAR/MNAR); Bayes networks | Slides | Murphy 8.6, ESL 9.6; Murphy 10 | |
| 4: 2/9 | Missing data, Bayes networks | HW 1 due | Murphy 8.6, ESL 9.6, Murphy 10 | HW 2 released 2/13 |
| 5: 2/16 | Ensembles, GLM variants | Slides | ISL 8.2, 6.2, 7.3, 7.7 | |
| 6: 2/23 | Causality in machine learning | HW 2 due 2/26@9am | Murphy 26.6; Scheines introduction | HW 3 to be released |
| 7: 3/2 | Neural networks 1 | Murphy 28; Deep Learning 2017 (slides) | ||
| 8: 3/9 | *No Class: mid-semester break* | HW 3 due | ||
| 9: 3/16 | *No Class: spring break* | |||
| 10: 3/23 | Neural networks 1 cont'd. | Proposals due 3/26@9am | Murphy 28; Deep Learning 2017 (slides) | WW A, HW 4 to be released |
| 11: 3/30 | Neural networks 2; support vector machines | Written work A due 4/2@9am | ISL 9 | |
| 12: 4/6 | Survival analysis; Hawkes processes | HW 4 due 4/9@9am | CASI 9; Hawkes process | WW B to be released |
| 13: 4/13 | Language modeling in R | Written work B due 4/16 end of day | Latent Dirichlet allocation; word2vec | |
| 14: 4/20 | Language modeling in R; relational learning | |||
| 15: 4/27 | Relational learning; class presentations | Paper due on 4/30 | ISL 10, Murphy 19 |
Note: bold indicates graded material