Joe Mercer

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[mathNEWS] New Music Roundup (5/25)

The theme this week is good music that came out in the last two weeks. But it’s surprisingly tough to find five good tracks that came out in the last two weeks, so I relaxed it to good music that I started listening to in the last two weeks.

 1. Ryan Caraveo - Floating

Ryan Caraveo is a rapper out of Seattle, and he doesn’t have a song with over 20,000 plays on Spotify, but he REALLY should. Go listen to his music. Floating is his newest single, and it sounds exactly like what a Seattle rap song should sound like (e.g. chill). Also, it’s also got an adorable music video.

 2. Cosmo Sheldrake - Rich (ft Anndreyah Vargas)

Cosmo Sheldrake released their EP Pelicans We last month and it sounds kind of like a more scattered Alt-j (which sounds unbelievable but trust me). Rich is the most polished song on the EP, and it lives up to the potential of the genre.

 3. Big Wild - Aftergold

I

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[mathNEWS] New Music Roundup (5/11)

Summer’s right around the corner, and I thought it would be nice to usher in the season with a couple of new tunes.

 1. Jon Bellion - Woodstock (Psychedelic Fiction)

Jon Bellion released a beautiful album in 2014 called The Definition, and after such fragile introspection you’d think he’d need to take a creative hiatus. But he didn’t. Instead he released Woodstock. It’s spacey and glitchy and perfect for listening to on repeat while hallucinating in the sun.

 2. NoMBe - California Girls

I hadn’t heard of NoMBe until this month, but I’m glad I found him. This is a song to play after the sun’s gone down; when the vibe is dark and sultry. It mixes well with The Neighbourhood and The Weeknd.

 3. Milk & Bone - Pressure

Milk & Bone is new band out of Montreal that makes chill, downtempo, cruising music. Pressure is a song for relaxing with your significant other on the porch. It’s hot and

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Team Selection (part 2)

In the first part of this blog post I discussed a type of problem called Team Selection. In rigorous terms, Team Selection is the problem of subdividing a set into optimal disjoint subsets. This is an extremely practical problem, because its solution could be applied in many real-life applications. A certain class of these applications can be simplified into grouping problems, as in the Noom Groups example from part one. In this case we relied on the fact that a group’s optimality was singularly dependent on its similarity, and were able to use the k-means clustering algorithm to create optimal groups.

A more interesting class of team selection applications are those that cannot be reduced to a grouping problem. In part one we saw this with the example of choosing roommates in a university residence. In these cases, the utility of the group is dependent upon more confusing factors than

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Using Machine Learning to Optimize for Teams with a Purpose

The previous article in this series explored how recommending teams of items could increase the quality of recommendation systems. This was particularly noteworthy in the case when an item’s preference was dependent upon other items, as in the case of steak and mashed potatoes vs steak and ice cream. In previous examples, the complicated nature of item relationships led to content-based recommendation systems that relied on confusing and domain-specific heuristics. This article will show how the objective nature of groups enables machine learning to simplify the recommendation system by inferring domain-specific logic from patterns in historical data.

A computer is said to learn if it can improve its performance at a task as it gains experience doing that task. It does this by inferring patterns in big data, and using those patterns to help it make more educated decisions. The learning

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Recommending Teams

Recommendation systems are an important part of daily life – we use them to help us browse the internet, buy products, and choose which movie to watch. The way these recommenders work is by predicting the preference that a user will have for an item [1], but the problem is that most recommenders rank items in isolation from one another. People do not rank items in isolation from one another. A person’s preference for an item can be highly dependent on the context that it’s being presented in. For example, I like ice cream more than mashed potatoes, but if there’s steak around, then I’d rather have the mashed potatoes. In other words, my preference for mashed potatoes is higher when the mashed potatoes are put in the context of steak. Recommendation systems can use this context to provide better recommendations.

The primary means that a recommendation system has to control context is

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[notes] The Toyota Way Fieldbook

 The Toyota Way Fieldbook (2005) by Jeffrey Liker and David Meier

Buy from Amazon

This book is intense. It’s meant as a companion to The Toyota Way (also by Jeffrey Liker), and I would recommend reading that book first for a higher level introduction. The fieldbook gets right down to the nitty gritty.

 Part I. Learning from Toyota

 Chapter 1: Background to the Fieldbook

“Toyota has contributed a new paradigm of manufacturing. “Lean production,” a term coined in The Machine That Changed the World, is widely considered the next big step in the evolution of manufacturing beyond Ford’s mass production.”

The Toyota Way documented Toyota’s Way. We decided the fieldbook should provide practical advice to those attempting to learn from The Toyota Way.”

 Overview of the Toyota Way Principles

 I. Philosophy as the Foundation

  1. Base your management decisions on a long-term philosophy, even at

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[notes] The Toyota Way

 The Toyota Way: 14 Management Principles from the World’s Greatest Manufacturer (2004) by Jeffrey Liker

Buy from Amazon

Great book. Personal commentary is at the bottom…


 Notes

 Part One: The World-Class Power of the Toyota Way

 Chapter 1: The Toyota Way: Using Operational Excellence as a Strategic Weapon

“The Toyota Production System is Toyota’s unique approach to manufacturing. It is the basis for much of the lean production movement that has dominated manufacturing trends (along with Six Sigma) for the last 10 years or so.”

“The key to their operations was flexibility. This helped Toyota make a critical discovery: when you make lead times short and focus on keeping production lines flexible, you actually get higher quality, better customer responsiveness, better productivity, and better utilization of equipment and space. While Ford’s traditional mass production looks good when

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Recommending Groups, or When the Whole is Greater than the Sum of its Parts - old

Perhaps the most important trend throughout history has been that of rising complexity [1]. Recommendation systems help manage this complexity by predicting the preference that a user would give to an item [2]. Recommendation systems are a part of daily life – we use them to help us browse the internet, buy products, and choose which movie to watch. In all of these cases, however, recommenders rank the items in isolation from one another. This is acceptable for things like books and movies, but becomes problematic for something like meals. The foods that make up a meal are highly inter-dependent, and a food recommender would have to take these relationships into account when recommending something to eat. For example, I may like ice cream more than mashed potatoes, but if I’m already being recommended a steak I’d prefer the mashed potatoes. In other words, sometimes a user’s preference

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Tether

When was the last time you called your mom? Or sent your grandma a card? Or emailed the coworkers from your last job?

If you’re like me, then friends and family are more important to you than anything, but you don’t spend nearly enough time letting them know that. I sympathize with you; maintaining relationships is more difficult and time-consuming than ever. As the world gets smaller, it seems like social networks just keep getting bigger and bigger: extended family, high school friends, college friends, old coworkers. And for each person that you want to stay in touch with, there’s an more and more ways of connecting with them. Should you email them? Send them a text? Snapchat them? It’s enough to overwhelm anybody. And when something overwhelms me, I tend to procrastinate it, which is just about the worst way to maintain relationships. In the best case, the pressure of not talking

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MetaWar

Warbot is a game that puts a meta spin on the classic card game that we all know and love.

The game mechanics are pretty simple. There are two players, each with money and a face down, shuffled deck of cards. The goal is to take all your opponents money. One player has a dealer chip. Each turn, the players ante in $1, draw a card, and look at it. Then the player with the dealer chip places a bet based on how likely they think it is that they have the high card. Based on the dealer’s bet, the second player can either choose to call the bet, or fold. If they fold, then the dealer keeps the ante, but if they call then both cards are revealed and the player with the higher card takes the entire pot. Then the dealer chip is rotates, and the game repeats. If the two cards are equal, then the round is a push and both players keep their bets.

Now let’s make it meta. Instead playing this game

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