2025 ASEE Annual Conference & Exposition

This paper presents the design of the introduction to machine learning (ML) course at the [university name removed]. This course is targeted toward first and second-year undergraduate students and thus has no prerequisite courses beyond the basic introduction to programming course; notably, there are no linear algebra nor probability prerequisite. A key design feature of the course is that it is entry level but still emphasizes the mathematical perspective of ML and conceptual understanding behind ML algorithms. This course presents the basic principles behind ML to make ML feel less like a "black box" and covers a range of applications, focusing on applications in the electrical engineering field. Students collect and interpret data, translate between textual and mathematical descriptions of systems, gain the skills necessary to implement and test ML functions in Python, and practice presenting data in easy-to-interpret plots.