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Introduction to Machine Learning (part 1 of 4)
Registration
Registration is now closed (this event already took place).
Details
This course will introduce students to the conceptual foundations
of machine learning (ML) and will describe a range of modern supervised and
unsupervised ML methods. We will discuss the advantages, limitations, and
appropriate uses of each and learn how to implement them using the Python
Keras ML library.
This course is appropriate for students with some exposure to coding and will
require a small amount of initial setup (installing Python and Keras). The
material will be especially useful for students who want to implement ML
methods for research or quantitative projects, but is open to all who are
interested.
Session format:
Lecture/discussion with optional coding exercises
What to expect:
Multiple Day Program.
Meet the facilitator:
Savannah Thais is an Associate Research Scholar at Princeton University, where she focuses on machine learning (ML). Her current work is centered on using geometric deep learning to build faster, more efficient data reconstruction algorithms for the High-Luminosity Large Hadron Collider and on incorporating physics constraints and expected symmetries into ML architectures. She also works in the AI Ethics space, focusing on regulation of emerging technology, informed consent for data collection and algorithm design/deployment, and community education. She is the founder and Research Director of Community Insight and Impact, an non-profit organization focused on data-driven community needs assessments for vulnerable populations and effective resource allocation. She sits on the Executive Board of Women in Machine Learning, the Executive Committee of the APS Forum on Physics and Society, is a Founding Editor of the Springer AI Ethics journal, and serves as the ML Knowledge Convener for the CMS experiment. She received her PhD in Physics from Yale University in 2019.
To request accommodations for this event, please contact the workshop or event facilitator at least 3 working days prior to the event.