A Statistician Teaches Deep Learning

TL;DR

This paper discusses how to effectively teach deep learning to statistics students by addressing cultural differences, providing a curriculum, and sharing teaching resources to bridge the gap between statistics and computer science.

Contribution

It offers a tailored syllabus, teaching tips, and resource recommendations to help statisticians incorporate deep learning into their curriculum.

Findings

Developed a recommended syllabus for teaching DL to statisticians

Provided practical homework examples and teaching resources

Discussed DL in the context of statistical research areas

Abstract

Deep learning (DL) has gained much attention and become increasingly popular in modern data science. Computer scientists led the way in developing deep learning techniques, so the ideas and perspectives can seem alien to statisticians. Nonetheless, it is important that statisticians become involved -- many of our students need this expertise for their careers. In this paper, developed as part of a program on DL held at the Statistical and Applied Mathematical Sciences Institute, we address this culture gap and provide tips on how to teach deep learning to statistics graduate students. After some background, we list ways in which DL and statistical perspectives differ, provide a recommended syllabus that evolved from teaching two iterations of a DL graduate course, offer examples of suggested homework assignments, give an annotated list of teaching resources, and discuss DL in the context of two research areas.