This course, a continuation of this fall's CS 294/Stat 242A, will focus on nonparametric methods. Examples that will be treated in detail include wavelets, splines, neural networks and support vector machines. We will also discuss theoretical concepts to support such architectures, including regularization, VC dimension and minimum description length, as well as tools for model selection and model averaging.
The prerequisites for this course include previous coursework in linear algebra, multivariate calculus, probability and statistics. Students will need to be familiar with Matlab, SPlus or a related matrix-oriented programming language.
The course will be a lecture course. The outline of topics is as follows:
Students will be required to complete bi-weekly homework assignments. These must be turned in on time to receive credit. Also, each student will carry out an independent project in an area subject to the instructor's approval.
Prof. Michael I. Jordan
jordan@{cs,stat}.berkeley.edu