Deeper Learning By Doing: Integrating Hands-On Research Projects Into a Machine Learning Course
This addresses the need for more practical, project-based learning in ML education, though it is incremental as it builds on existing teaching methods.
The authors tackled the challenge of enhancing student engagement in machine learning courses by integrating hands-on research projects, resulting in a structured approach that includes experimental design, report writing, and peer review.
Machine learning has seen a vast increase of interest in recent years, along with an abundance of learning resources. While conventional lectures provide students with important information and knowledge, we also believe that additional project-based learning components can motivate students to engage in topics more deeply. In addition to incorporating project-based learning in our courses, we aim to develop project-based learning components aligned with real-world tasks, including experimental design and execution, report writing, oral presentation, and peer-reviewing. This paper describes the organization of our project-based machine learning courses with a particular emphasis on the class project components and shares our resources with instructors who would like to include similar elements in their courses.