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Please see individual pages for upcoming WORKSHOPS & EVENTS for format (in-person or virtual). If it is unsafe and/or unallowable to host in-person trainings, we will transition to virtual workshops. Best browser to register: CHROME.

Artificial Intelligence (AI) & Machine Learning (ML) Webinars

Are you interested in understanding AI and Machine Learning? Would you like inspiration on how to integrate it into your High School classes? Not sure where to start? Access our recorded webinars!


Selected as the National AI Institute for Foundations of Machine Learning (IFML) by the National Science Foundation (NSF) in 2020, we are partnering with the Texas Advanced Computing Center (TACC) to widen the knowledge and teaching of Machine Learning and AI in High Schools. Join us as we kick off the summer with our AI / ML webinar series!

Please register at no-cost for each webinar separately using the links listed by the dates and titles below. All registered participants who attend the webinar will receive 1 hour of CPE credits per session.


What is Artificial Intelligence and Machine Learning?

(06/14/2021, 10:00-11:00am CT) An overview and brief look at the history of AI and ML with Joydeep Biswas.




Applications of AI

(06/28/2021, 10:00-11:00am CT) Natural Language Processing with Raymond Mooney.





AI and Society

(07/08/2021, 10:00-11:00am CT) Ethical issues with Ken Fleischmann.





About the Institute for Foundations of Machine Learning - a National AI Research Institute

Designated by the National Science Foundation (NSF) in 2020, we are the National AI Institute for Foundations of Machine Learning (IFML). We develop the key foundational tools for the next decade of AI innovation leveraging the combined forces of The University of Texas at Austin, University of Washington, Wichita State University, and Microsoft Research. Our researchers create new algorithms that can help machines learn on the fly, change their expectations as they encounter people and objects in real life, and even bounce back from deliberate attempts by adversaries to manipulate datasets.

This program is supported by the National Science Foundation Award FAIN 2019844.