• The course notes from (most of) the first lecture are now available on the Handouts page. A 1 page handout about the course is also there.
• Did you get an e-mail titled "Welcome to the stat 538 mailing list"? If not, send me mail ASAP.
• Homework 1 is now posted.

Who is this class for?
This class is the third of a sequence intended for statistics and biostatistics students (the previous two courses being STAT 534, STAT 535), or for other graduate students with an interest in statistics, algorithms and computing. The focus of the present course will be on optimization with applications to parameter estimation and to modern machine learning and pattern recognition methods. A more detailed description of the topics is given here. The grade is based on a project (40%), homework (40%) and class participation (20%). The students are encouraged to choose a project topic related to their own research interest, but they need to discuss this topic with the instructor before starting on the project.

Prerequisites
EITHER STAT 534 and STAT 535 OR

• A course in probability, including basic notions of multivariate analysis (conditional probability, marginals, expectation, variance)
• Notions of statistics: Maximum Likelihood Estimation, MAP estimation, priors, likelihood, estimating parameters of usual distribution (normal, multinomial)
• Calculus: partial derivatives, the chain rule, vectors and matrices, matrix multiplications, gradient
• Algorithms and data structure at a basic level (arrays, lists, sets, O( ) notation).
• Medium ability with a computer programming language (like C, C++, Java or Matlab, Splus, R)

Instructor: Marina Meila   mmp@stat.washington.edu

Lectures: Tuesdays & Thursdays 11:30 - 12:50 in LOW 106

Office hours (may change!): Mondays 2-3 in Padelford B - 321