Toward an Open-source Toolkit for Machine Learning Education

Abstract

Machine Learning (ML) has become one of the highly participated courses at the undergraduate level in Computer Science. Open-source ML libraries make it easy for students to implement papers, share ideas, and conduct experiments on large scale datasets. With the emergence of public dataset portals (such as Kaggle, Amazon Datasets, and Google Datasets Search), the open-source community has produced many useful, high-quality libraries (such as Scikit-Learn, PyTorch, Keras, and Tensorflow among others). These open-source tools aim to make state-of-the-art ML algorithms and large-scale datasets accessible to all. While these ML libraries and datasets can benefit many undergraduate students in their pursuit of data-related careers, the task of choosing them for instructional purposes can be daunting for two reasons. First, all of these tools have advantages, drawbacks, and many overlapping issues. There is no single tool or dataset that covers all of the ML instructional needs. Second, due to the rapid advancements in the field, instructors often find a lack of comprehensive guidelines or standards on evaluating the instructional usability and real-world performance of open-source tools. How can these libraries and tools be integrated to aid the instructional activities of both classical machine learning as well as deep learning? This BOF will provide a platform for the discussion of the development of an open-source toolkit to support the teaching and learning of ML at the undergraduate level.