Announcements
- Homework 4 will be posted on Wednesday morning.
- Homework 3 will be due THURSDAY October 29. Homework 4 will be posted Tuesday October 27, and will be due next Tuesday, November 3
- Handout 4 updated (Mon Oct 26, 5:15pm)
What will the course be about?
- The theme of
STAT 535 is graphical probability models (aka belief
networks), an outstanding example of statistics and algorithms at
work together. Emphasis will be on applying graphical models to data
analysis, and on the algorithmic, computational and practical aspects
of these models.
- In addition, you will occasionally explore other topics in the
probability and statistics of discrete structures and
you will learn and implement some clever
general purspose algorithms.
Who is this class for?
This class is the first in the
Statistics PhD Computing 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
Either STAT 534 OR
- 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)
Instructor: Marina Meila
mmp at stat dot washington dot edu
Lectures: Tuesdays & Thursdays 11:30 - 12:50 in Johnson 026
Office hours: Marina Meila, 2-3 pm in PDL B-321
Course home page: http://www.stat.washington.edu/courses/stat535/fall09 (this page)
Class mailing list: stat535a_au09@uw.edu (uses your @u mailing address by default)
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