Linear Digressions
Un pódcast de Ben Jaffe and Katie Malone
Categorías:
289 Episodo
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Network effects re-release: when the power of a public health measure lies in widespread adoption
Publicado: 15/3/2020 -
Causal inference when you can't experiment: difference-in-differences and synthetic controls
Publicado: 9/3/2020 -
Better know a distribution: the Poisson distribution
Publicado: 2/3/2020 -
The Lottery Ticket Hypothesis
Publicado: 23/2/2020 -
Interesting technical issues prompted by GDPR and data privacy concerns
Publicado: 17/2/2020 -
Thinking of data science initiatives as innovation initiatives
Publicado: 10/2/2020 -
Building a curriculum for educating data scientists: Interview with Prof. Xiao-Li Meng
Publicado: 2/2/2020 -
Running experiments when there are network effects
Publicado: 27/1/2020 -
Zeroing in on what makes adversarial examples possible
Publicado: 20/1/2020 -
Unsupervised Dimensionality Reduction: UMAP vs t-SNE
Publicado: 13/1/2020 -
Data scientists: beware of simple metrics
Publicado: 5/1/2020 -
Communicating data science, from academia to industry
Publicado: 30/12/2019 -
Optimizing for the short-term vs. the long-term
Publicado: 23/12/2019 -
Interview with Prof. Andrew Lo, on using data science to inform complex business decisions
Publicado: 16/12/2019 -
Using machine learning to predict drug approvals
Publicado: 8/12/2019 -
Facial recognition, society, and the law
Publicado: 2/12/2019 -
Lessons learned from doing data science, at scale, in industry
Publicado: 25/11/2019 -
Varsity A/B Testing
Publicado: 18/11/2019 -
The Care and Feeding of Data Scientists: Growing Careers
Publicado: 11/11/2019 -
The Care and Feeding of Data Scientists: Recruiting and Hiring Data Scientists
Publicado: 4/11/2019
In each episode, your hosts explore machine learning and data science through interesting (and often very unusual) applications.