Linear Digressions

Linear Digressions

Por: Katie Malone
Linear Digressions is a podcast about machine learning and data science. Machine learning is being used to solve a ton of interesting problems, and to accomplish goals that were out of reach even a few short years ago. 896520
320 episodios disponibles

Latest episodes of the podcast Linear Digressions

Mostrando página 3 de 16

Protecting Individual-Level Census Data with Differential Privacy 18/05/2020
Causal Trees 11/05/2020
The Grammar Of Graphics 04/05/2020
Gaussian Processes 27/04/2020
Keeping ourselves honest when we work with observational healthcare data 20/04/2020
Changing our formulation of AI to avoid runaway risks: Interview with Prof. Stuart Russell 13/04/2020
Putting machine learning into a database 06/04/2020
The work-from-home episode 29/03/2020
Understanding Covid-19 transmission: what the data suggests about how the disease spreads 23/03/2020
Network effects re-release: when the power of a public health measure lies in widespread adoption 15/03/2020
Causal inference when you can't experiment: difference-in-differences and synthetic controls 09/03/2020
Better know a distribution: the Poisson distribution 02/03/2020
The Lottery Ticket Hypothesis 23/02/2020
Interesting technical issues prompted by GDPR and data privacy concerns 17/02/2020
Thinking of data science initiatives as innovation initiatives 10/02/2020
Building a curriculum for educating data scientists: Interview with Prof. Xiao-Li Meng 02/02/2020
Running experiments when there are network effects 27/01/2020
Zeroing in on what makes adversarial examples possible 20/01/2020
Unsupervised Dimensionality Reduction: UMAP vs t-SNE 13/01/2020
Data scientists: beware of simple metrics 05/01/2020
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