This song is about overfitting, a central concept in machine learning. It is in the style of mountain music and, when listening, one should think about someone staying up all night trying to get their algorithm to work, but it just won't stop overfitting! The music and lyrics are by Cynthia Rudin from Duke University and was one of three data science songs by Dr. Rudin that won the grand prize and 1st place in the song category in the 2023 A-mu-sing competition.
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 cartoon to spark a discussion about the normal equations in the matrix approach to linear models. The cartoon was created by Kylie Lynch, a student at the University of Virginia. The cartoon won first place in the non-song categories of the 2023 A-mu-sing competition.
A poem for encouraging discussion on aspects of making predictions using regression models (e.g. treating possible non-linearity). The poem was written in 2023 by Dane C Joseph from George Fox University in Oregon.
A humorous cartoon by American cartoonist Jon Carter in 2018 which may be used for in-class discussions about interpreting time series plots. The drawing indicates confusion about what each axes represents, since the plot itself indicates the x-axes labels time, but the axes itself says "customer intelligence" and there is no scale on either axesThe cartoon is free to use in non-profit educational settings.
A cartoon that can be helpful as a vehicle to discuss how finding a good data visualization to tell the story of a study’s results is an art – even if it must be combined with the science of statistics to give an appropriate impression. The cartoon was used in the July 2022 CAUSE cartoon caption contest and the winning caption was submitted by John Montagu, a student at University of Colorado, Boulder.. An alternative caption: "While each plot was from a different perspective, it was the aggregation of the plots that told the whole story." was submitted by Jim Alloway from EMSQ Associates, and reinforces the idea that it may take several graphs to give a full picture of a data set.The cartoon was drawn by British cartoonist John Landers (www.landers.co.uk) based on an idea by Dennis Pearl from Penn State University.
A cartoon to teach about the graphical displays of discrete data - especially using bar charts. Cartoon by John Landers (www.landers.co.uk) based on an idea from Dennis Pearl (The Ohio State University). Free to use in the classroom and on course web sites.Cartoon was revised in March, 2023 to include a histogram amongst the graphs on the wall.
"WON OVeR" is a poem by Lawrence Mark Lesser from The University of Texas at El Paso. The poem was written in 2022 and originally published in the January 2023 Journal of Humanistic Mathematics. The poem highlights the unexpected occurrence of the constant 1/e in two classic probability problems: “secretary problem”/”marriage problem” and “hats derangement problem”. The poem could be used either to motivate students to learn about those particular problems or to cap things off after working through them.
A joke to teach the idea that the average of independent measurements are more reliable than individual measurements from the same process. The joke should help start a discussion of the importance of the independence assumption in this idea. The joke was written by Dennis Pearl, Penn State University and Larry Lesser, The University of Texas at El Paso in September, 2022.