A Statistician Teaches Deep Learning

G. Jogesh Babu, David Banks, Hyunsoon Cho, David Han, Hailin Sang, Shouyi Wang

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Article number47
JournalJournal of Statistical Theory and Practice
Volume15
Issue number2
DOIs
StatePublished - Jun 2021

All Science Journal Classification (ASJC) codes

  • Statistics and Probability

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