2020 AMS Annual Meeting

 Agenda

Machine learning has become prominent within the atmospheric and environmental sciences over the past years.  Numerical model parameterization, empirical predictive modeling, data post-processing, and many other sub-fields have benefitted from the rapid introduction of machine learning techniques into our community.  It is likely that the importance of machine learning will only continue to expand as computers become more efficient and new methods are developed.  Whether you want to apply machine learning within your own research or gain a better understanding of methods being used by your colleagues, this course will help prepare you for an era where machine learning has an ever-growing impact on our field.

This is the first of three courses covering machine learning within atmospheric and environmental science.  The primary goal of this course is to introduce fundamental concepts within machine learning.  The expectation is that after this course, the attendee will be comfortable discussing and applying basic machine learning methods.  We will cover common data preparation techniques, linear models, supervised learning algorithms such as random forests and neural networks, deep learning, and some basic concepts in model interpretation.  Attendees will be instructed by active researchers within the atmospheric science/machine learning community, and will have opportunities to apply their new knowledge to hands-on programming modules throughout the course.

Instructors include atmospheric scientists and machine learning experts from various academic institutions and government labs.

Participants are expected to bring their own laptops to be able to participate in the hands-on exercises.  Separate instructions will be sent out to participants regarding additional set-up instructions before the workshop.

For more information, please contact either Ben Toms ([email protected]) or Amanda Burke ([email protected]).