**Instructors:**Marina Meila and Alejandro Murua

**Brief description**

This course presents several landmark methods of Machine Learning
in a unified framework. We aim:

(1) to introduce the ideeas and mathematics that are fundamental to
any problem that involves learning from data, and

(2) to teach a selection of important and general methods of machine
learning

Hence, we will emphasize equally the techniques themselves and the
common underlying concepts. Many of these concepts are rooted in
probability and statistics, hence we will often adopt a probabilistic
perspective to describe the problem setup and the solution. Then we
will analyze how the solution is obtained from an algorithmic
perspective.

**Lectures:**
Tuesdays 3:30 - 4:50, Fridays 1:00 - 2:20 in MGH 295

**Office hours:**TBA

**Prerequisites:** basic notions of probability (at the level of STAT
390/391/394), basics of algorithms and complexity, multivariate
calculus. We will introduce the statistics concepts that we are going
to use during the course, as well as notions of information theory.

**Format:** Two 1.5 hour lectures + discussions weekly. Typically, the
instructors will start by presenting each topic, then we will progress
towards an open discussion. The reading materials will be course notes
plus original research and tutorial papers.

**Graded:** 3 credits C/NC based on two mini-projects + homework + class participation.

**Course home page: http://www.ms.washington.edu/stat592/winter04**
(this page)