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 projectbased 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.
[1]
Natalia Gimelshein,et al.
PyTorch: An Imperative Style, High-Performance Deep Learning Library
,
2019,
NeurIPS.
[2]
S. Headden,et al.
Motivation Matters: How New Research Can Help Teachers Boost Student Engagement.
,
2015
.
[3]
Turning Software Engineers into Machine Learning Engineers
,
2020
.
[4]
Christian D. Schunn,et al.
Learning Together While Designing: Does Group Size Make a Difference?
,
2012
.
[5]
L. S. Schulman,et al.
THE CARNEGIE FOUNDATION FOR THE ADVANCEMENT OF TEACHING.
,
1940,
Science.