Short Course: Machine Learning in Python for Environmental Science Problems: Advanced Topics

Sunday, 12 January 2020, 8:30 a.m.
Location TBD

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 course covers advanced topics within machine learning, and builds upon the introductory course offered the day prior.  It is advised that attendees have some prior experience with machine learning, but the course will also be useful for those generally interested in how state-of-the-art machine learning methods are applied within the atmospheric and environmental sciences.  We will cover unsupervised learning methods such as clustering and neural network autoencoders, methods for interpreting machine learning models, and physics-aware machine learning.  After this course, attendees will be comfortable discussing and applying advanced machine learning methods.  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 (ben.toms@colostate.edu) or Amanda Burke (aburke1@ou.edu).

Short Course/Workshop Registration

All short course/workshop attendees must register and wear a badge/ribbon. Short course/workshop registration is not included in the 99th Annual Meeting registration, and short course/workshop registration does not include registration for the 99th AMS Annual Meeting.

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