Linear Digressions

A podcast by Ben Jaffe and Katie Malone

Categorie:

289 Episodio

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

    Pubblicato: 15/3/2020
  2. Causal inference when you can't experiment: difference-in-differences and synthetic controls

    Pubblicato: 9/3/2020
  3. Better know a distribution: the Poisson distribution

    Pubblicato: 2/3/2020
  4. The Lottery Ticket Hypothesis

    Pubblicato: 23/2/2020
  5. Interesting technical issues prompted by GDPR and data privacy concerns

    Pubblicato: 17/2/2020
  6. Thinking of data science initiatives as innovation initiatives

    Pubblicato: 10/2/2020
  7. Building a curriculum for educating data scientists: Interview with Prof. Xiao-Li Meng

    Pubblicato: 2/2/2020
  8. Running experiments when there are network effects

    Pubblicato: 27/1/2020
  9. Zeroing in on what makes adversarial examples possible

    Pubblicato: 20/1/2020
  10. Unsupervised Dimensionality Reduction: UMAP vs t-SNE

    Pubblicato: 13/1/2020
  11. Data scientists: beware of simple metrics

    Pubblicato: 5/1/2020
  12. Communicating data science, from academia to industry

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

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

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

    Pubblicato: 8/12/2019
  16. Facial recognition, society, and the law

    Pubblicato: 2/12/2019
  17. Lessons learned from doing data science, at scale, in industry

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

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

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

    Pubblicato: 4/11/2019

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In each episode, your hosts explore machine learning and data science through interesting (and often very unusual) applications.

Visit the podcast's native language site