Statistical Perspectives in Teaching Deep Learning from Fundamentals to Applications
David Han
The University of Texas at San Antonio
Nathan Kim
The University of Texas at San Antonio
The use of AI, ML and Deep Learning (DL) have gained a lot of attention and become increasingly popular in many areas of application. Historically ML and theory had strong connections to statistics; however, the current deep learning context is mostly in computer science perspectives and lacks statistical perspectives. In this work, we address this research gap and discuss how to teach DL to the next generation of statisticians. We first describe some backgrounds and how to get motivated. We discuss different terminologies in computer science and statistics, and how deep learning procedures work without getting into mathematics. In response to a question regarding what to teach, we address organizing deep learning contents and focus on the statistician's view; form basic statistical understandings of the neural networks to the latest hot topics on uncertainty quantifications for prediction of DL, which has been studied in the Bayesian frameworks. Furthermore, we discuss how to choose computational environments and help develop programming skills for the students. We also discuss how to develop homework incorporating the idea of experimental design.