Statistics vs Machine Learning for Complex Problems: White, Black or Grey Boxes?

Wednesday, 9 January 2019: 12:15 PM-1:15 PM
North 224A (Phoenix Convention Center - West and North Buildings)
Hosts: (Joint between the Town Hall Meetings; and the 18th Conference on Artificial and Computational Intelligence and its Applications to the Environmental Sciences )
Organizer:
Elizabeth A. Satterfield, NRL, Marine Meteorology Division, Monterey, CA
Facilitator:
Philippe E. Tissot, Texas A&M University-Corpus Christi, Conrad Blucher Institute, Corpus Christi, TX
Panelists:
Elana Fertig, Johns Hopkins University, Division of Biostatistics and Bioinformatics, Baltimore, MD; David John Gagne II, NCAR, Boulder, CO; Sebastian Lerch, Heidelberg Institute for Theoretical Studies, Computational Statistics Group, Heidelberg; Amy McGovern, University of Oklahoma, School of Computer Science, Norman, OK and Elaine Yang, Jupiter, Boulder, CO

With the rapid growth of Machine Learning (ML) and its recent prominence in Environmental Sciences,  this town hall aims to better understand how such methods can work in concert with or complement more traditional statistical methods.  This town hall aims to better define the challenges of ML methods in environmental science applications, discuss how to determine which problems are better suited to particular methods, explore how statistical method and ML can work together and focus on how we can increase interpretability of ML algorithms.

- Indicates paper has been withdrawn from meeting
- Indicates an Award Winner
See more of: Town Hall Meetings