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.
So long, and thanks for all the fish
July 26, 2020
00:35:44
A Reality Check on AI-Driven Medical Assistants
July 19, 2020
00:14:00
A Data Science Take on Open Policing Data
July 13, 2020
00:23:44
Procella: YouTube's super-system for analytics data storage
July 6, 2020
00:29:48
The Data Science Open Source Ecosystem
June 29, 2020
00:23:06
Rock the ROC Curve
June 21, 2020
00:15:52
Criminology and Data Science
June 15, 2020
00:30:57
Racism, the criminal justice system, and data science
June 7, 2020
00:31:36
An interstitial word from Ben
June 5, 2020
00:05:59
Convolutional Neural Networks
May 31, 2020
00:21:55
Stein's Paradox
May 24, 2020
00:27:02
Protecting Individual-Level Census Data with Differential Privacy
May 18, 2020
00:21:19
Causal Trees
May 11, 2020
00:15:27
The Grammar Of Graphics
May 4, 2020
00:35:38
Gaussian Processes
April 27, 2020
00:20:55
Keeping ourselves honest when we work with observational healthcare data
April 20, 2020
00:19:08
Changing our formulation of AI to avoid runaway risks: Interview with Prof. Stuart Russell
April 13, 2020
00:28:58
Putting machine learning into a database
April 6, 2020
00:24:22
The work-from-home episode
March 29, 2020
00:29:06
Understanding Covid-19 transmission: what the data suggests about how the disease spreads
March 23, 2020
00:25:25
Network effects re-release: when the power of a public health measure lies in widespread adoption
March 15, 2020
00:26:40
Causal inference when you can't experiment: difference-in-differences and synthetic controls
March 9, 2020
00:20:48
Better know a distribution: the Poisson distribution
March 2, 2020
00:31:51
The Lottery Ticket Hypothesis
Feb. 23, 2020
00:19:45
Interesting technical issues prompted by GDPR and data privacy concerns
Feb. 17, 2020
00:20:26
Thinking of data science initiatives as innovation initiatives
Feb. 10, 2020
00:17:27
Building a curriculum for educating data scientists: Interview with Prof. Xiao-Li Meng
Feb. 2, 2020
Running experiments when there are network effects
Jan. 27, 2020
00:24:45
Zeroing in on what makes adversarial examples possible
Jan. 20, 2020
00:22:51
Unsupervised Dimensionality Reduction: UMAP vs t-SNE
Jan. 13, 2020
00:29:34
Data scientists: beware of simple metrics
Jan. 5, 2020
00:24:47
Communicating data science, from academia to industry
Dec. 30, 2019
00:26:15
Optimizing for the short-term vs. the long-term
Dec. 23, 2019
00:19:24
Interview with Prof. Andrew Lo, on using data science to inform complex business decisions
Dec. 16, 2019
00:27:46
Using machine learning to predict drug approvals
Dec. 8, 2019
00:25:00
Facial recognition, society, and the law
Dec. 2, 2019
00:43:09
Lessons learned from doing data science, at scale, in industry
Nov. 25, 2019
00:28:00
Varsity A/B Testing
Nov. 18, 2019
00:36:00
The Care and Feeding of Data Scientists: Growing Careers
Nov. 11, 2019
00:25:19
The Care and Feeding of Data Scientists: Recruiting and Hiring Data Scientists
Nov. 4, 2019
00:20:16
The Care and Feeding of Data Scientists: Becoming a Data Science Manager
Oct. 28, 2019
Oct. 21, 2019
Kalman Runners
Oct. 13, 2019
00:15:59
What's *really* so hard about feature engineering?
Oct. 6, 2019
00:21:18
Data storage for analytics: stars and snowflakes
Sept. 30, 2019
00:15:22
Data storage: transactions vs. analytics
Sept. 23, 2019
00:16:08
GROVER: an algorithm for making, and detecting, fake news
Sept. 16, 2019
00:18:28
Data science teams as innovation initiatives
Sept. 9, 2019
00:15:21
Can Fancy Running Shoes Cause You To Run Faster?
Sept. 1, 2019
00:30:15
Organizational Models for Data Scientists
Aug. 25, 2019
00:23:09
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