A Statistician Teaches Deep Learning
It helps statisticians and their students bridge the gap to deep learning for career relevance, but it is incremental as it focuses on educational adaptation rather than new research.
The paper addresses the cultural gap between deep learning and statistics by providing guidance on teaching deep learning to statistics graduate students, including a recommended syllabus, homework examples, and teaching resources.
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.