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UID:20200328T081015EDT-28909fbcea@www.amherst.edu/node/763413
DTSTAMP:20200328T121015Z
CATEGORIES:Amherst Event
DESCRIPTION:Machine learning is revolutionizing the sciences\, but most exi
sting methods require large amounts of human-generated training data to su
cceed. In this talk\, we will introduce the unsupervised clustering proble
m\, which requires an algorithm to make predictions without training data.
We will discuss some classical methods for clustering before introducing
a couple of new approaches. Throughout\, connections with graph theory\, F
ourier analysis and probability theory will be developed. We will also dem
onstrate\napplications to image processing and remote sensing.\n\nJames M.
Murphy is an assistant professor of mathematics at Tufts University. His
research interests include theoretical machine learning and applied harmon
ic analysis. He works on problems in unsupervised and semi-supervised lear
ning\, high-dimensional probability theory\, image and signal processing\,
graph theory and frame theory.
DTSTART:20200206T213000Z
DTEND:20200206T213000Z
LOCATION:Seeley G. Mudd Building\, 207
SUMMARY:Math Colloquium: 'Unsupervised Clustering\, Harmonic Analysis and A
pplications' - James Murphy
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