Introduction to Machine Learning (Part 4 of 5)

This mini-course will provide a comprehensive introduction to machine learning. Part 1 covers introductory concepts such as what is machine learning, why is it used, software packages and supervised versus unsuperivsed learning. Attendees will learn how to train simple machine learning models in Part 2. Part 3 covers model evaluation and refinement. Artificial neural networks are introduced during Part 4. The mini-course concludes with a hackathon during Part 5. Participants will have the opportunity to work on a small, end-to-end machine learning project chosen from one of multiple domains.

Attendees should have some familiarity with Python and basic calculus.

Meet the Facilitator:
(1) Brian Arnold was born and raised in Minnesota, where he attended the University of Minnesota --Twin Cities and earned a degree in plant biology. Fascinated by the concept of using genomic data to understand evolution, Brian continued studying plants during his PhD at Harvard University and later studied bacterial genomics during his postdoc at the Harvard T.H. Chan School of Public Health. Afterwards, he worked as a Senior Bioinformatics Scientist at Harvard where he continued working on genomics and taught introductory data science workshops. Brian joined Princeton University in 2020 as Schmidt DataX fellow where he works on biomedical cloud computing with large data sets.

(2) Prior to coming to Princeton, Vineet worked at Brooks Instrument where he implemented models developed by research scientists, automated data-collection procedures throughout the research lab, and developed applications for visualization of data collected through several research projects. He has also worked at Bank of America where he assisted with the development of data analysis tools, and at the Center for Language Education & Research at Michigan State University where he developed globally-deployed solutions for language learning, teaching, and testing.

(3) Christina Peters is a postdoctoral researcher in the Department of Computer & Information Sciences at the University of Delaware. Her research focuses on developing and applying machine learning techniques to answer data-intensive questions in experimental physics and observational astronomy.

(4) Amy Winecoff received her Ph.D. in psychology and neuroscience from Duke University. After graduate school, she was an assistant professor at Bard College, where she taught neuroscience, abnormal psychology, and research methods. After leaving academia, she conducted research and developed machine learning models for government agencies such as DARPA and the U.S. Air Force to explain and predict human behavior. As a senior data scientist at True Fit and Chewy, she developed product recommendation and search systems. She also conducted quantitative user research to assess how users’ psychology informs their evaluation of algorithmic predictions. Amy is passionate about diversity and inclusion in the technology industry.

(5) Gage DeZoort is a fifth year graduate student in Physics working with Dan Marlow. He collaborates with Boris Hanin and Isobel Ojalvo. His research involves using machine learning and other modern computational techniques to search for new particles, reconstruct physics objects, and improve physics analysis workflows.

What to Expect:
Intensive Workshop

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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Co-hosted with: PICSciE/Research Computing

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