Machine Learning in Python for Environmental Science Problems

This virtual short course will develop skills in applying Python libraries to process their data, train a variety of ML models, generate predictions, and evaluate and interpret their trained models for physical understanding.

January 12–13, 2022 at 10:00 AM - 6:00 PM Eastern Time (Virtual)

Registration close date: TBD
Participant cap: TBD

REGISTRATION HAS NOT OPENED

Registration rates:

(Rates have not yet been determined for this course.)

Registration policy:

AMS requires a valid payment to be made within 5 days of the start of a course or sooner if registration has reached capacity. You will be contacted by AMS staff if payment is required. Refunds will not be issued to attendees within 7 days of the start of a course. Registrations are not transferable or exchangeable.

Course Description:

Interest in artificial intelligence (AI), machine learning (ML), and deep learning (DL) in the environmental sciences has grown rapidly in conjunction with the increased presence of AI in our daily lives. Many people now want to apply ML to their own data and problems but do not know where to start. This 2-day short course will develop participants skills in applying python libraries to process their data, train a variety of ML models, generate predictions, and evaluate and interpret their trained models for physical understanding. Participants will interact with real-world data and the ML pipeline through a series of Google Collaboratory notebooks that will enable thorough exploration of the data and methods. Participant understanding of the concepts will be reinforced with hands-on exercises at the end of each lecture that require implementing many of the techniques introduced while grappling with the challenges of real-world environmental datasets.

Day 1 of the short course will cover AI and ML fundamentals and introduce Python libraries commonly employed for data analysis, ML, and deep learning (DL). Day 2 will provide participants with the option of instruction on more advanced topics in ML/DL following a similar lecture/exercise format as employed on Day 1. Additional Instructions will be emailed to registered attendees before the course begins.

Participants will need access to Zoom through either the web or desktop application.

Instructors:

William Chapman
William Chapman

National Center for Atmospheric Research

Laura Ko
Laura Ko

University of Miami/CIMAS and NOAA/AOML/HRD

Dylan Steinkruger
Dylan Steinkruger

FLASH Scientific Technology

Karthik Kashinath
Karthik Kashinath

NERSC, Lawrence Berkeley National Laboratory, and NVIDIA

Tirthankar Chakraborty
Tirthankar Chakraborty

Pacific Northwest National Laboratory

Nishant Yadav
Nishant Yadav

Sustainability and Data Sciences (SDS) Laboratory, Northeastern University

Callie McNicholas
Callie McNicholas

University of Washington