Educational Resources for AI and Machine Learning
Short courses, tutorials, conferences, webinars, courses, and data sets
Short Courses and Tutorials
Explainable AI Short Course
Dates: 10 May – 21 June 2021
6 sessions, 90 minutes each – LIVE via telecon at 1:30-3:00 pm (Central)
Instructor: Ryan Lagerquist
The short course will consist of six 90-minute lectures, each accompanied by slides and a Colab notebook with open-source Python code. All Python examples will focus on weather prediction.
Dates: 26 – 29 July, 2021
Offered by AI2ES and NCAR
Topics include Trustworthy AI for Environmental Science; Explainable, Robust, Physics-based AI; AI, Ethics, and Trust; Case Studies; R2O Tips and Tricks.
Machine Learning in Python for Environmental Science Problems
Dates: 8 – 9 April , 2021 at 10:00 AM – 6:00 PM Eastern Time (Virtual)
Host: American Meteorological Society
2nd NOAA Workshop On Leveraging AI In Environmental Sciences
Dates: 30 July 2020 – 25 February 2021
Host: National Oceanic and Atmospheric Administration’s STAR Center for Satellite Applications and Research
Workshop includes presentations, posters, panel discussions, and tutorials.
Tutorial sessions: 22 September 2020 and 20 October 2020. More tutorial sessions are planned.
CIRA Short Course On Machine Learning For Weather And Climate
Dates: 16 September – 21 October 2020
Instructors: Dr. Ryan Lagerquist (CIRA Boulder and NOAA GSL) and Dr. Imme Ebert-Uphoff (CIRA Fort Collins and Colorado State University)
Links are available for recorded video lectures, GitHub, and Jupyter Notebooks on the CIRA website.
Artificial Intelligence For Earth System Science (AI4ESS) Summer School
Dates: 22-26 June 2020
Host: National Center for Atmospheric Research (Boulder, Colorado, USA)
Presentation slides and recordings are available here.
Conferences and Webinars
20th Conference On Artificial Intelligence For Environmental Science
Dates: 10-14 January 2021 in New Orleans, Louisiana, USA as part of the American Meteorological Society’s annual meeting
Host: Sponsored by the American Meteorological Society and organized by theAMS Committee on Artificial Intelligence Applications to Environmental Science
2nd NOAA Workshop On Leveraging AI In Environmental Sciences
Dates: 30 July 2020 – 25 February 2021
Host: National Oceanic and Atmospheric Administration’s STAR Center for Satellite Applications and Research
Workshop includes presentations, posters, panel discussions, and tutorials.
19th Conference On Artificial Intelligence For Environmental Science
Dates: 13 – 16 January 2020
Part of the American Meteorological Society’s 2020 annual meeting.
Presentation recordings are online.
Courses
University of Oklahoma – Norman
Computer Science Course CS 5970: AI, Ethics, and Geoethics
Dates: Spring semester 2021, online only
Instructor: Dr. Amy McGovern, Lloyd G. and Joyce Austin Presidential Professor, School of Computer Science and School of Meteorology, and Director of the NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography, University of Oklahoma
Del Mar College
Computer Science, Engineering and Advanced Technology Department, Course: Introduction to Artificial Intelligence (GISC-1372-709FA)
Instructor: Dr. Phillip Davis
This is the first AI course as part of a 14-hour Level I Certificate program that supports the College’s Associate in Science in Geographical Information Systems degree. Introduction to Artificial Intelligence (GISC-1372-709FA) covers AI and Machine Learning and will be taught online by course designer Phillip Davis, Ph.D., DMC Professor of Computer Science. The course is one of five making up the AI Level I Certificate program.
To learn more about the certificate program and the AI course, contact Dr. Davis at pdavis@delmar.edu
.To register for DMC Summer I and II and Fall 2021 credit courses, go to www.delmar.edu/registration for admissions and registration requirements, deadlines and other information.
Weather and Climate Datasets for AI Research
A collection of weather and climate datasets for AI research curated by Stephan Rasp.