Announcements

Old exams with solutions are posted here: exam 11, sol exam 11,exam 10,sol exam 10
The exam will be openbook. You can bring any materials on paper but
NO ELECTRONICS.

Office hour today Tuesday in PDL B321 4:306pm. NO office hour on Wednesday.
Who is this class for? This class is the second of a
sequence intended for statistics and biostatistics students (the
previous course being STAT 535) with an interest in statistical learning, algorithms for statistical inference, and models for multidimensional data, as well as
for other graduate students with an interest in statistics, algorithms
and computing. The focus of the present course will be on
supervised learning, and its connections to optimization. A more detailed description of the topics is given here.
The grade is based on homework (60%), final exam (25%) and class
participation (15%) (approximately).
Prerequisites EITHER 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) at the level of STAT 534
Instructor: Marina Meila
mmp@stat.washington.edu
Lectures: Tuesdays & Thursdays 11:30  12:50 in LOW 101
Office hours: Wednesdays 23 in Padelford B  321 (tentative)
Course home page: http://www.stat.washington.edu/course/stat538/winter12 (this page)
