Intro to Machine Learning, Part 1 of 5

This is an intensive five-day workshop. To register for this intensive workshop, please register on part 1 of 5.

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.

What to expect:


Meet the facilitator:
Savannah Thais received her PhD in Particle Physics from Yale University
where she focused on implementing novel machine learning methods for particle
reconstruction at the Large Hadron Collider. She is currently a postdoctoral
researcher in Research Computing at Princeton with the IRIS-HEP project. She
works on physics-informed machine learning, AI for social good, and
regulation and ethics of emerging technology.

To request accommodations for this event, please contact the workshop or event facilitator at least 3 working days prior to the event.

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