An Dang, Special to The Denisonian
Denison will debut a new minor in machine learning and artificial intelligence next semester.
This program is designed to give students in technical fields a specialized edge in the evolving job market. The curriculum, approved by a faculty vote in December, creates a formal pathway for students to study the algorithms behind the current boom in AI technology.
The program was approved by the university’s governance process at an unusually rapid pace, taking 138 days from its official proposal to final approval. However, the program’s architect, Dr. Matt Kretchmar, emphasizes that this speed reflects high demand and strong faculty collaboration, not a rushed curriculum.
Kretchmar, a professor of computer science who spearheaded the proposal, explained that while the formal approval was a “sprint,” the academic planning began months earlier.
“The 138 days is not the most important part,” Kretchmar said. “By far, the most important part is the content in the minor and what we’re trying to achieve pedagogically.”
According to Kretchmar, the process began with a “white paper” drafted over the summer, which he circulated among faculty in the mathematics, data analytics, and physics departments. By the start of the school year, a working group had already formed to ensure the curriculum would fit Denison’s liberal arts model before it ever reached the Academic Affairs Council.
For students like Louis Ta ‘28, a sophomore data analytics major, the new minor offers a necessary credential for a competitive industry.
“I think it is going to help Denison catch up with the current AI boom of the job market,” Ta said. “I already really like AI and machine learning … and I think it is a fascinating field to study.”
Ta intends to declare the minor, though as a rising junior, he expressed concern about fitting the requirements into his remaining schedule before graduation.
His concern is valid, as the new minor is not intended for all students. Kretchmar noted that the program is highly rigorous, requiring a “high barrier” of prerequisites, including linear algebra and statistics. “It is more mathematics than it is computing, actually,” Kretchmar said.
Because of these demands, the department anticipates a small, focused cohort. Kretchmar estimates the program will accommodate about 12 minors per year, starting with a group of roughly six sophomores in the upcoming semester.
The curriculum is distinct from the general AI courses currently offered to non-majors. Kretchmar noted that the minor requires a strong foundation in mathematics, specifically linear algebra, and is designed for students already pursuing technical majors.
Addressing concerns about whether the university has the hardware to support such resource-intensive study, Kretchmar explained that most fundamental machine learning algorithms can run on standard laptops. For advanced work involving large language models, the department has high-end computing units and access to the Ohio Supercomputer Center.
Kretchmar acknowledges that the current surge in AI interest follows a “hype” cycle, comparing it to previous booms in technology. However, he argues that the underlying technology of large language models has staying power that justifies a permanent place in the curriculum.
“The technology already exists, and is only going to get better,” Kretchmar said. “There is going to be a need for people to understand this technology, to do research in this technology and to be able to apply this technology.”
Ultimately, Kretchmar believes the minor’s greatest value lies in its approach. Beyond teaching students how to build these systems, the curriculum is designed to push them to question the consequences of the technology itself.
“I think too often, people in the tech industry ask the question, ‘Can we?’” Kretchmar said. “And they should ask the question, ‘Should we?’”