Linear Digressions

Podcast autorstwa Ben Jaffe and Katie Malone

Kategorie:

289 Odcinki

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

    Opublikowany: 15.03.2020
  2. Causal inference when you can't experiment: difference-in-differences and synthetic controls

    Opublikowany: 9.03.2020
  3. Better know a distribution: the Poisson distribution

    Opublikowany: 2.03.2020
  4. The Lottery Ticket Hypothesis

    Opublikowany: 23.02.2020
  5. Interesting technical issues prompted by GDPR and data privacy concerns

    Opublikowany: 17.02.2020
  6. Thinking of data science initiatives as innovation initiatives

    Opublikowany: 10.02.2020
  7. Building a curriculum for educating data scientists: Interview with Prof. Xiao-Li Meng

    Opublikowany: 2.02.2020
  8. Running experiments when there are network effects

    Opublikowany: 27.01.2020
  9. Zeroing in on what makes adversarial examples possible

    Opublikowany: 20.01.2020
  10. Unsupervised Dimensionality Reduction: UMAP vs t-SNE

    Opublikowany: 13.01.2020
  11. Data scientists: beware of simple metrics

    Opublikowany: 5.01.2020
  12. Communicating data science, from academia to industry

    Opublikowany: 30.12.2019
  13. Optimizing for the short-term vs. the long-term

    Opublikowany: 23.12.2019
  14. Interview with Prof. Andrew Lo, on using data science to inform complex business decisions

    Opublikowany: 16.12.2019
  15. Using machine learning to predict drug approvals

    Opublikowany: 8.12.2019
  16. Facial recognition, society, and the law

    Opublikowany: 2.12.2019
  17. Lessons learned from doing data science, at scale, in industry

    Opublikowany: 25.11.2019
  18. Varsity A/B Testing

    Opublikowany: 18.11.2019
  19. The Care and Feeding of Data Scientists: Growing Careers

    Opublikowany: 11.11.2019
  20. The Care and Feeding of Data Scientists: Recruiting and Hiring Data Scientists

    Opublikowany: 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.

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