Teaching Computational Machine Learning (without Statistics)

Anonymous

Abstract: This paper presents an undergraduate machine learning course that emphasizes algorithmic understanding and programming skills while assuming no statistical training. Emphasizing the development of good habits of mind, this course trains students to be independent machine learning practitioners through an iterative, cyclical framework for teaching concepts while adding increasing depth and nuance. Beginning with unsupervised learning, this course is sequenced as a series of machine learning ideas and concepts with specific algorithms acting as concrete examples. This paper also details course organization including evaluation practices and logistics.

Keywords: machine learning education, undergraduate education, computational machine learning

TL;DR: This paper presents an undergraduate machine learning course that assumes no statistical knowledge and that trains students to be independent machine learning practitioners through an iterative, cyclical framework for teaching concepts.