Exploration of Teaching Reform in Machine Learning Courses in Applied Universities

This article proposes the following reform ideas for machine learning courses based on the talent cultivation goals of applied universities: optimizing course content with application as the goal; Aiming at the forefront of the discipline and innovating teaching cases; Reconstruct teaching mode with student-centered approach; Enrich assessment dimensions and improve feedback mechanisms. These reform measures aim to improve the teaching quality of machine learning courses, enhance the learning effectiveness of students, and better adapt to the demand for high-quality applied talents in the new era.