Lecture materials for the ETHZ course '860-0033-00L Big Data for Public Policy FS2021' (cf.Course syllabus)
Signing sheet for students presentation
Notebooks on
| N° | Date | Lecture | Materials |
|---|---|---|---|
| 0 | Feb 25 | Class overview | Slides in html, pdf |
| 1 | Feb 25 | Statistical Learning [part 1] | Slides in html, pdf |
| 2 | Mar 4 | Statistical Learning [part 2] | Slides in html, pdf |
| 3 | Mar 4 | Scraping and APIs | Notebook: .ipynb, html |
| 4 | Mar 11 | Intro to ML: regressions | Slides in html, pdf; Notebook: .ipynb, html |
| 5 | Mar 18 | Unsupervised learning | Slides in html, pdf; Notebook: .ipynb, html |
| 6 | Mar 25 | Classification | Slides in html, pdf; Notebook: .ipynb, html |
| 7 | Apr 1 | [NLP1] Text analysis essentials | Slides in html, pdf |
| 8 | Apr 15 | [NLP2] | Slides in html, pdf |
| 9 | Apr 15 | Text analysis | Notebook: .ipynb, html |
| 10 | Apr 22 | Advanced ML | Slides in html, pdf; Notebook: .ipynb, html |
| 11 | Apr 29 | Research design | Slides in html, pdf; |
| 12 | Apr 29 | Developing a Dash application | Notebook: .ipynb, html |
| 13 | May 6 | Heterogeneous treatment effect | Slides in html, pdf |
| 14 | May 20 | Advanced causal methods | Notebook: .ipynb, html |
| 15 | May 27 | The Limitations of ML | Slides in html, pdf |