Tuning the Parameters: A Maritime-Tuned Machine Learning Course

Abstract

In machine learning (ML) education, the choice of which datasets to utilize for student assignments and projects is critical for student success and meeting course learning outcomes. Poorly chosen datasets leave students disinterested and questioning the applicability of ML in real-world situations specific to their intended endeavors post academia. Additionally, some datasets demand much effort for preprocessing and a steep learning curve for understanding, which detracts from the ML experience and leaves students frustrated. As maritime and marine engineering programs expand to include ML in their curricula with the plan of addressing industry trends in, for example, autonomy and defense, it is important to calibrate the ML course accordingly with relevant datasets and assignments. We develop a maritime-specific course in undergraduate ML (taken in the sixth semester) to engage students whose interests include maritime and marine industries. In support of our course, we compose several maritime-specific ML mini projects employing the popular and convenient Google Colab platform and make them publicly available within a GitHub repository. A hybrid of programming and report writing, each mini project utilizes the same publicly available maritime-related dataset-one that requires little preprocessing and, we show, is conducive for demonstrating many of the concepts vital to classical ML as well as some topics in deep learning. Using the same dataset for many assignments fosters a feeling of student comfortability, promotes comparing the performances of different ML algorithms, and provides a low barrier of entry after the initial assignment. Our paper is both a detailed syllabus of a first course in maritime-focused ML and a how-to guide for effective use of the mini projects we have developed. Going further, it is a solution to the mini projects, as it reports on ML algorithms' performances, how the choices of key tuning parameters affect said performances, and how and why algorithms perform the way they do. Included in the paper is a student reflection authored by a US Coast Guard license student in engineering to offer instructors a unique student perspective and insight into the efficacy of the course design. Our hope is that colleagues interested in teaching a similar course at their own institutions can adopt our methods, and thereby reduce their preparation work and increase student engagement.