BEGIN:VCALENDAR
VERSION:2.0
CALSCALE:GREGORIAN
PRODID:iCalendar-Ruby
BEGIN:VEVENT
CATEGORIES:Academics,Natural & Applied Sciences Division,Mathematics and St
 atistics
DESCRIPTION:“On the graph Laplacian and its applications in machine learnin
 g – Part 1: spectral graph theory”  by Gabriel Chen PhD\, Mathematics & Sta
 tistics Department\n\nThe Laplacian matrix of a graph is an important conce
 pt in machine learning. Let G be an undirected\, weighted graph with weight
  matrix W (e.g.\, a social network in which weights indicate how well two p
 eople know each other). The Laplacian matrix of the graph is defined as L =
  D - W\, where D is a diagonal matrix with diagonal entries being the row s
 ums of W. The graph Laplacian matrix (L) has many interesting properties. F
 or example\, it is symmetric and positive semidefinite\, and the multiplici
 ty of the eigenvalue 0 coincides with the number of connected components in
  the graph. This is the first of a two-talk series\, where we will introduc
 e the concepts associated to the Laplacian matrix in a graph setting and th
 en present some theoretical properties as well as interpretations of the gr
 aph Laplacian matrix. The talk is most accessible to people who have taken 
 one semester of linear algebra\; knowledge of graph concepts is helpful but
  not needed.
DTEND:20240208T220000Z
DTSTAMP:20260412T064207Z
DTSTART:20240208T210000Z
GEO:42.786861;-86.104963
LOCATION:A. Paul Schaap Science Center\, 1000
SEQUENCE:0
SUMMARY:Mathematics and Statistics Colloquium: Gabriel Chen\, Ph.D.
UID:tag:localist.com\,2008:EventInstance_45403088426835
URL:https://calendar.hope.edu/event/mathematics_and_statistics_colloquium_g
 abriel_chen_phd
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