Linear Digressions

We're excited to welcome a guest, Tim Head, who is one of the maintainers of the scikit-optimize package. With all the talk about optimization lately, it felt appropriate to get in a few words with someone who's out there making it happen for python. Relevant links: https://scikit-optimize.github.io/ http://www.wildtreetech.com/

It's a funny thing to realize, but data science modeling is usually about either explainability, interpretation and understanding, or it's about predictive accuracy. But usually not both--optimizing for one tends to compromise the other. Leo Breiman was one of the titans of both kinds of modeling, a statistician who helped bring machine learning into statistics and vice versa. In this episode, we unpack one of his seminal papers from 2001, when machine learning was just beginning to take root, and talk about how he made clear what machine learning could do for statistics and why it's so important. Relevant links: http://www.math.snu.ac.kr/~hichoi/machinelearning/(Breiman)%20Statistical%20Modeling--The%20Two%20Cultures.pdf

You've got an optimization problem to solve, and a less-than-forever amount of time in which to solve it. What do? Use a heuristic optimization algorithm, like a hill climber or simulated annealing--we cover both in this episode! Relevant link: http://www.lizsander.com/programming/2015/08/04/Heuristic-Search-Algorithms.html

If modeling is about predicting the unknown, optimization tries to answer the question of what to do, what decision to make, to get the best results out of a given situation. Sometimes that's straightforward, but sometimes... not so much. What makes an optimization problem easy or hard, and what are some of the methods for finding optimal solutions to problems? Glad you asked! May we recommend our latest podcast episode to you?

Ok, this episode is only sort of about DEADLY RADIOACTIVE GAS. It's mostly about multilevel modeling, which is a way of building models with data that has distinct, related subgroups within it. What are multilevel models used for? Elections (we can't get enough of 'em these days), understanding the effect that a good teacher can have on their students, and DEADLY RADIOACTIVE GAS. Relevant links: http://www.stat.columbia.edu/~gelman/research/published/multi2.pdf

Continuing the discussion of how polls do (and sometimes don't) tell us what to expect in upcoming elections--let's take a concrete example from the recent past, shall we? The Brexit referendum was, by and large, expected to shake out for "remain", but when the votes were counted, "leave" came out ahead. Everyone was shocked (SHOCKED!) but maybe the polls weren't as wrong as the pundits like to claim. Relevant links: http://www.slate.com/articles/news_and_politics/moneybox/2016/07/why_political_betting_markets_are_failing.html http://andrewgelman.com/2016/06/24/brexit-polling-what-went-wrong/

Not sure if you heard, but there's an election going on right now. Polls, surveys, and projections about, as far as the eye can see. How to make sense of it all? How are the projections made? Which are some good ones to follow? We'll be your trusty guides through a crash course in election forecasting. Relevant links: http://www.wired.com/2016/06/civis-election-polling-clinton-sanders-trump/ http://election.princeton.edu/ http://projects.fivethirtyeight.com/2016-election-forecast/ http://www.nytimes.com/interactive/2016/upshot/presidential-polls-forecast.html?rref=collection%2Fsectioncollection%2Fupshot&action=click&contentCollection=upshot&region=rank&module=package&version=highlights&contentPlacement=5&pgtype=sectionfront

Genomics data is some of the biggest #bigdata, and doing machine learning on it is unlocking new ways of thinking about evolution, genomic diseases like cancer, and what really makes each of us different for everyone else. This episode touches on some of the things that make machine learning on genomics data so challenging, and the algorithms designed to do it anyway.

Hot enough for you? Climate models suggest that it's only going to get warmer in the coming years. This episode unpacks those models, so you understand how they work. A lot of the episodes we do are about fun studies we hear about, like "if you're interested, this is kinda cool"--this episode is much more important than that. Understanding these models, and taking action on them where appropriate, will have huge implications in the years to come. Relevant links: https://climatesight.org/

Last week’s episode on artificial intelligence gets a huge payoff this week—we’ll explore a wonderful couple of papers about all the ways that artificial intelligence can go wrong. Malevolent actors? You bet. Collateral damage? Of course. Reward hacking? Naturally! It’s fun to think about, and the discussion starting now will have reverberations for decades to come. https://www.technologyreview.com/s/601519/how-to-create-a-malevolent-artificial-intelligence/ http://arxiv.org/abs/1605.02817 https://arxiv.org/abs/1606.06565