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.  --- Send in a voice message: https://anchor.fm/databytes/message Support this podcast: https://anchor.fm/databytes/support

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#50: Extreme Classification: All You Need Is Some Hash (Functions)

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. --- Send in a voice message: https://anchor.fm/databytes/message Support this podcast: https://anchor.fm/databytes/support

#49: Extreme Classification: Going at MACH Speed (Part 1)

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).  --- Send in a voice message: https://anchor.fm/databytes/message Support this podcast: https://anchor.fm/databytes/support

#48: Where Moneyball Meets Footy

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.  --- Send in a voice message: https://anchor.fm/databytes/message Support this podcast: https://anchor.fm/databytes/support

#47: Domoic Acid Testing -- A Crabshoot?

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. --- Send in a voice message: https://anchor.fm/databytes/message Support this podcast: https://anchor.fm/databytes/support

#46: Finding Your (Niche) Board Games

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. --- Send in a voice message: https://anchor.fm/databytes/message Support this podcast: https://anchor.fm/databytes/support

#45: Learning Publicly, with Private Data

We sit down with Dr. Jon Krohn to chat about his work as a Chief Data Scientist at untapt, his newly published bestseller "Deep Learning Illustrated", and his teaching/research. --- Send in a voice message: https://anchor.fm/databytes/message Support this podcast: https://anchor.fm/databytes/support

#44: A Conversation with Jon Krohn

In this episode, Professor Albert Y. Kim of Smith College describes his post-PhD journey, which included a stint at Google Adwords before academic posts at Reed College, Middlebury College, Amherst College, and Smith College. --- Send in a voice message: https://anchor.fm/databytes/message Support this podcast: https://anchor.fm/databytes/support

#43: To Google and Back

Dr. Derek Feng joins us again to discuss the two metrics by which we align all statistical/machine learning methods -- interpretability versus predictive ability. In a world where black box methods reign supreme, what does learning mean? --- Send in a voice message: https://anchor.fm/databytes/message Support this podcast: https://anchor.fm/databytes/support

#42: Black in the Box

Guest Dylan O'Connell joins us today to talk about a recent surprising, but legitimate Democratic primary poll result done by Monmouth University. We discuss different perspectives on how to approach a data point that doesn't fit in with the others.  --- Send in a voice message: https://anchor.fm/databytes/message Support this podcast: https://anchor.fm/databytes/support

#41: What to do with Outliers

Outdoor-cat owners know all too well the unpleasantries of dealing with what the cat dragged in. A self-proclaimed machine learning novice proves that you don't need to be a pro to set up a smart cat door that prevents the cat from bringing prey into your home. --- Send in a voice message: https://anchor.fm/databytes/message Support this podcast: https://anchor.fm/databytes/support

#40: Making a DIY ML-Controlled Cat Door

We take a deep dive into the poster child for black-box machine learning methods, namely Deep Patient: an unsupervised learning method that uses denoising auto-encoders as the means for extracting salient features in electronic health records, which in turn can then be used to predict health outcomes. We do our best to explain what on earth the previous sentence meant. --- Send in a voice message: https://anchor.fm/databytes/message Support this podcast: https://anchor.fm/databytes/support

#39: Rolling in the Deep Patient

In this episode, we talk about how a statistical concept that you would learn about in an introductory course was misused in court.  The error led to dire consequences in the case of Sally Clark who was charged in the deaths of two of her children. --- Send in a voice message: https://anchor.fm/databytes/message Support this podcast: https://anchor.fm/databytes/support

#38: The Misuse of Statistics in Court

In this episode, we talk about Susan's new job as a Data Scientist!  She recently transitioned from academia to industry and we discuss her experience with searching for positions, interviewing, and her first few weeks in her new role. --- Send in a voice message: https://anchor.fm/databytes/message Support this podcast: https://anchor.fm/databytes/support

#37: Susan Starts a New Job

In this episode, we talk about some machine learning startups to pay attention to this year. --- Send in a voice message: https://anchor.fm/databytes/message Support this podcast: https://anchor.fm/databytes/support

#36: What's New in Machine Learning Startups

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. Support this podcast: https://anchor.fm/databytes/support

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