Abstract:

Despite a rapid expansion of machine learning (ML) across fields and industries, little is known about how to best prepare students for work in ML. Most ML courses today are taught at the college or professional level using a theoretical programming approach. Existing educational resources may not be sufficient or preferable for many audiences, particularly those who do not have a strong computer science background, who wish to attain basic understanding of and ability to use ML. We present learnings from a beginner level, semester-long actionable machine learning course taught at Massachusetts Institute of Technology meant to be accessible for students with minimal computer science knowledge. Based on analysis of survey responses and student projects, we find 5 core concepts (Multilayer Networks, Convolutional Neural Networks, Transfer Learning, Recurrent Neural Networks, and Embeddings & Generative Models) and 8 core skills (scoping a problem, choosing datasets, creating datasets, choosing models, modifying models, creating models, modifying learning rates, and training & testing) that helped lead to student self-efficacy as independent ML developers when mastered. We conclude by discussing implications of this research on effective course design and educational efforts for beginner level university courses in ML.

Keywords:

Machine learning education, computing education, computational thinking, actionable pedagogical framework, experiential learning, machine learning labs.