Category: Events

High school students invited to explore machine learning at U of M Machine Learning Summer Camp

For the 5th year in a row, the Minnesota Center for Financial and Actuarial Mathematics (MCFAM) within the University of Minnesota’s School of Mathematics is hosting Machine Learning (ML) Summer Camps for high school students. Like last summer, the camps will be held virtually. The three different weeklong camps are co-sponsored by MCFAM, the School of Mathematics and the School of Statistics.

Campers in 11th and 12th grade benefit from a mix of formal instruction on ML concepts and learn techniques and coding in Python in small group projects, ranging from breast cancer diagnosis algorithms that predict benign and malignant tumors to predicting the winner of Pokémon.

At the end of each day, campers meet with guest speakers and gain insight on how ML methods are used by local companies such as Securian, C.H. Robinson, US Bank, Travelers, 3M, and Amazon. Even in the pandemic, last year’s campers said they were “very excited by the opportunity to connect remotely with other high school students (both locally and nationally) who share our same interests.”

Dr. Melissa Lynn, an Assistant Professor of Mathematics, Computer Science and Statistics at Gustavus Adolphus, was one of the instructors of last year’s camp and will be an instructor again this year. Having been involved in this camp for 5 years, she said that for her, “the highlight is getting to watch the students’ presentations at the end of the camp. I am always impressed by how much they learn in only five days, and it is great to see them put it to practice on real datasets!”

MinneAnalytics members can look for opportunities to volunteer and to register their kids for the camps.

Camp Dates:
June 14-June 18: Girls Only Camp (Girl-Identified, nonbinary, transgender girls)
June 21-June 25: All Genders Camp
June 28-July 2: Advanced All Genders Camp

For all the details, visit the Machine Learning Summer Camp webpage.

If you’d like to learn about volunteer opportunities, contact Dr. Gary Hatfield at

Student Machines Defeat Humans in Spring Edition of MinneMUDAC Data Science Challenge

Many of us look forward to the excitement of filling out March Madness brackets each year, but this year MinneAnalytics took that thrill to a new level. For the special Spring 2021 edition of the organization’s MinneMUDAC Student Data Science Challenge, student teams were invited to compete for bragging rights and cash prizes by building predictive models to make their picks. These machine-generated picks were also compared to a separate pool of human predictions to see how they would fare.

The results may not seem all that surprising: the predictive models created by students proved to be more successful at predicting the outcomes than their human competitors. Teams had two chances to make predictions – the main challenge began with the First Round of the NCAA Men’s Basketball Championship. A second “Sweet Sixteen” challenge was added once the championship was down to 16 teams. Student-led teams won both challenges decisively.

“I’m not saying you’ll be working for machines in the future,” said MinneAnalytics cofounder Dan Atkins. “I’m saying you’ll be working for some of these brilliant students that are creating these machines.”

The highest overall score in the main challenge was achieved by a team of undergraduates from the University of St. Thomas College of Arts and Sciences with a score of 171. They used a multivariate linear probability model with effective field goal percentage, turnover rate, and conference strength being their key independent variables. The team was comprised of Economics majors Mark Neuman and Patrick McLean, along with accounting major/economics minor Scott Kalthoff. Tyler Schipper, assistant professor in data analytics and economics, served as the teams faculty advisor.

An undergraduate team from the University of Minnesota – College of Science and Engineering took home first place in the Sweet Sixteen Challenge with a score of 116. Their methodology focused on picking upsets with seeding, 3 point percentage, and defensive efficiency as their key independent variables. Team members included Industrial and Systems Engineering (ISyE) students Sam Casey, Connor Fell, Isaac McCarney, and Will Titus, with faculty advisor Ankur Mani.

The top team among those in the graduate division was from University of Iowa, achieving a score of 145 in the main competition (4th place overall) and 112 in the Sweet Sixteen Challenge (2nd place overall). The team was comprised of student Tyler Dennis, who is pursuing an MS in Statistics, and faculty advisor Matt Bognar. According to Dennis, “we utilized a simple LDA model that used only information presented in an empty bracket, and the seasons from 2011-2012 through 2018-2019 were used to train the model.”

While the human pool overall was not as strong as the student machines, there were some standouts: Technovation[MN] executive director Lisa Schlosser, who has spoken at MinneAnalytics events in the past, came in second in the main challenge with an impressive score of 157. In the Sweet Sixteen Challenge, an 8th grader, Ray Anderson finished with a strong score of 104 (5th place overall).

MinneMUDAC is generally an annual challenge organized each fall, but last year the competition had to be called off due to pandemic concerns. The idea for a special spring challenge was brought to MinneAnalytics when Joe Lambrecht, a Computer Science and Data Analytics major at University of St. Thomas, contacted Dan Atkins.

“We were looking for fun ways to inspire students to explore data modeling and analysis outside of their coursework, and with March Madness just a month away, the timing was perfect,” said Lambrecht, who is the special programs director of the UST Data Science and Analytics Club. “Originally, we were looking at a school-wide competition, but I thought there was a lot of potential for the event beyond just one university.”

MinneMUDAC is presented by MinneAnalytics and the Midwest Undergraduate Data Analytics Competition (MUDAC). Thank you to all of the students, advisors, and professionals who took part in the competition. View the complete final standings at this link.