Announcements
What will the course be about?  The theme of
STAT 535 is unsupervised learning with emphasis on
clustering, i.e finding groups in data, and on graphical
probability models (aka belief networks). Both are outstanding
examples of statistics and algorithms at work together.
Who is this class for? This class is the first in the
Statistics PhD Learning sequence, but it is regularly attended by
other students with an interest in machine learning, graphical models
and the connection of statistics to algorithms and optimization.
Prerequisites
 A course in probability, including basic notions of multivariate
analysis (conditional probability, marginals).
 Algorithms and data
structure at a basic level (arrays, lists, sets, O( ) notation).
 Knowledge of a computer programming language (like
C, C++, Java, Matlab, R, Splus) For
Statistics students, taking STAT
534 is a good way to get up to speed in algorithms and
programming.
Instructor: Marina Meila
mmp at stat dot washington dot edu
Lectures: Tuesdays & Thursdays 11:30  12:50 in GUG 204
Office hours: Monday 23pm in PDL B321
Course home page: http://www.stat.washington.edu/courses/stat535/fall11 (this page)
Class mailing list: stat535a_au11 at UW
