Estimation Principles

A music video designed to assist in teaching the basics of Multi-Armed Bandits, which is a type of machine learning algorithm and is the foundation for many recommender systems. These algorithms spend some part of the time exploiting choices (arms) that they know are good while exploring new choices (think of an ad company choosing an advertisement they know is good, versus exploring how good a new advertisement is). The music and lyrics were written by Cynthia Rudin of Duke University and was one of three data Science songs that won the grand prize and first in the song category for the 2023 A-mu-sing competition.

The lyrics are full of double entendres so that the whole song has another meaning where the bandit could be someone who just takes advantage of other people! The author provides these examples of some lines with important meanings:
"explore/exploit" - the fundamental topic in MAB!
"No regrets" - the job of the bandit is to minimize the regret throughout the game for choosing a suboptimal arm
"I keep score" - I keep track of the regrets for all the turns in the game
"without thinking too hard,"  - MAB algorithms typically don't require much computation
"no context, there to use," - This particular bandit isn't a contextual bandit, it doesn't have feature vectors 
"uncertainty drove this ride." - rewards are probabilistic
"I always win my game"  - asymptotically the bandit always finds the best arm
"help you, decide without the AB testing you might do" - Bandits are an alternative to massive AB testing of all pairs of arms
"Never, keeping anyone, always looking around and around" - There's always some probability of exploration throughout the play of the bandit algorithm

This song is about the k-nearest neighbors algorithm in machine learning. This popular algorithm uses case-based reasoning to make a prediction for a current observation based on nearby observations. The music and lyrics were written by Cynthia Rudin from Duke University who was accompanied by  Dargan Frierson, from University of Washington in the audio recording. The song is one of three data science songs written by Cynthia Rodin that took the grand prize and first prize in the song category in the 2023 A-mu-sing competition.

A song to discuss how a confidence interval made for a population parameter will be biased if the sample is biased (e.g. starting with a random sample of n=100 but then having individuals drop out one at a time based on a non-ignorable reason).  The song was written IN MARCH 2019 by Lawrence Lesser, The University of Texas at El Paso, and Dennis Pearl, Penn State University, using the mid-20th century recursive folk song "99 Bottles of Beer." The idea for the song came from an article by Donald Byrd of University of Indiana in the September 2010 issue of Math Horizons where he suggested using the song for various learning objectives in Mathematics Education.

A cartoon suitable for use in teaching about confidence intervals and the quality of estimates made by a model. The cartoon is number 2311 (May, 2020) from the webcomic series at xkcd.com created by Randall Munroe. Free to use in the classroom and on course web sites under a Creative Commons attribution-non-commercial 2.5 license.