Linear Digressions

289 Épisodes

1. Network effects re-release: when the power of a public health measure lies in widespread adoption

   Publié: 15/03/2020
2. Causal inference when you can't experiment: difference-in-differences and synthetic controls

   Publié: 09/03/2020
3. Better know a distribution: the Poisson distribution

   Publié: 02/03/2020
4. The Lottery Ticket Hypothesis

   Publié: 23/02/2020
5. Interesting technical issues prompted by GDPR and data privacy concerns

   Publié: 17/02/2020
6. Thinking of data science initiatives as innovation initiatives

   Publié: 10/02/2020
7. Building a curriculum for educating data scientists: Interview with Prof. Xiao-Li Meng

   Publié: 02/02/2020
8. Running experiments when there are network effects

   Publié: 27/01/2020
9. Zeroing in on what makes adversarial examples possible

   Publié: 20/01/2020
10. Unsupervised Dimensionality Reduction: UMAP vs t-SNE

    Publié: 13/01/2020
11. Data scientists: beware of simple metrics

    Publié: 05/01/2020
12. Communicating data science, from academia to industry

    Publié: 30/12/2019
13. Optimizing for the short-term vs. the long-term

    Publié: 23/12/2019
14. Interview with Prof. Andrew Lo, on using data science to inform complex business decisions

    Publié: 16/12/2019
15. Using machine learning to predict drug approvals

    Publié: 08/12/2019
16. Facial recognition, society, and the law

    Publié: 02/12/2019
17. Lessons learned from doing data science, at scale, in industry

    Publié: 25/11/2019
18. Varsity A/B Testing

    Publié: 18/11/2019
19. The Care and Feeding of Data Scientists: Growing Careers

    Publié: 11/11/2019
20. The Care and Feeding of Data Scientists: Recruiting and Hiring Data Scientists

    Publié: 04/11/2019