A Statistician Teaches Deep Learning

Journal Article (Journal Article)

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.

Full Text

Duke Authors

Cited Authors

  • Babu, GJ; Banks, D; Cho, H; Han, D; Sang, H; Wang, S

Published Date

  • June 1, 2021

Published In

Volume / Issue

  • 15 / 2

Electronic International Standard Serial Number (EISSN)

  • 1559-8616

International Standard Serial Number (ISSN)

  • 1559-8608

Digital Object Identifier (DOI)

  • 10.1007/s42519-021-00193-0

Citation Source

  • Scopus