Join us on December 8 at 12 pm EDT for a talk entitled "Deep learning: opening the black box" by Jennifer A. Hoeting. This lecture is presented by SWB and the Committee on International Relations in Statistics (CIRS).
About the lecture: Deep learning algorithms are often presented as black box algorithms. Many cartoon sketches of deep learning are available, but deep learning is rarely translated into the mathematical framework required by most statisticians to understand the topic. In this lecture, we will:
- Open the black box and explore deep learning from a statistical viewpoint.
- We will explore the types of problems when you consider using a machine or deep learning algorithm and when traditional inferential statistical methods may be preferred.
- Provide suggestions on how to get started applying machine and deep learning algorithms to your own data with links to recommended software.
About the presenter: Jennifer A. Hoeting leads Hoeting Consulting. She is a Professor Emeritus of Statistics at Colorado State University and an Adjunct Professor of Statistics at the University of California Santa Cruz. Hoeting's textbook, Computational Statistics (co-authored by Geof H. Givens), has been adopted as a course textbook at more than 120 universities in the US and over 30 other countries. Hoeting is an elected Fellow of the American Statistical Association and received the Distinguished Achievement Medal from the American Statistical Association’s Section on Statistics and the Environment.
The link to register is here: https://amstat.zoom.us/