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DataBytes Jessi & Susan
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- Wissenschaft
Data science, big data, artificial intelligence, machine learning… they’re all the rage. In this podcast, Jessi Cisewski-Kehe and Susan Wang, 2 statisticians, give you a perspective on what’s happening in the realm of all things data. Random bantering included.
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#50: Extreme Classification: All You Need Is Some Hash (Functions)
In part 2 of this saga on extreme classification, we get into the weeds on how MACH is able to magically handle such massive classification problems. The title says it all -- hash functions are the magical ingredient. We provide a step-by-step view of how one might come up with the MACH algorithm from first principles.
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#49: Extreme Classification: Going at MACH Speed (Part 1)
In this episode, Dr. Derek Feng drops by to chat about a recent paper on a divide-and-conquer approach (Merged-Averaged Classifiers via Hashing) to massive classification problems. In part 1 (of 2 episodes), we describe the general problem solved by and strategy taken by MACH, wherein the original large classification problem is broken down into smaller-sized classification problems. Next week in the second episode, we talk about more technical details of how the division of labor works, and why it works.
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#48: Where Moneyball Meets Footy
We've long heard about the waves that statistics has made in baseball. But what about soccer? In this episode, we summarize a few applications of statistics in European football (or American soccer).
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#47: Domoic Acid Testing -- A Crabshoot?
Domoic acid has plagued shellfish and other wildlife along the Pacific coastline in recent years. Testing for domoic acid concentration in crabs on a regular basis has become important for determining when crabs and their viscera can be safely consumed. Unlike many other common hypothesis tests, the setup used for domoic acid testing is based on the sample maximum rather than the sample mean. In this episode, we critique the testing methodology.
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#46: Finding Your (Niche) Board Games
In this episode, we discuss how two statisticians used data from BoardGameGeek.com to put together their own board game recommendation engine, specifically designed to stay away from mainstream recommendations.
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#45: Learning Publicly, with Private Data
In this episode, Dr. Derek Feng discusses the general issue of data privacy in the age of big data, including topics of differential privacy and federated learning.
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Support this podcast: https://podcasters.spotify.com/pod/show/databytes/support