Mon-Wed 5:00-6:30pm, in
Hall, on the UC Berkeley campus.
(First meeting is Wed Sept 4, 2013.)
Matrices are a popular way to model data (e.g., term-document data,
people-SNP data, social network data, machine learning kernels, and so on),
but the size-scale, noise properties, and diversity of modern data presents
serious challenges for many traditional deterministic matrix algorithms.
The course will cover the theory and practice of randomized algorithms for
large-scale matrix problems
arising in modern massive data set analysis (i.e., Randomized Numerical
Topics to be covered include: underlying theory, including the
Johnson-Lindenstrauss lemma, random sampling and projection algorithms, and
connections between representative problems such as matrix multiplication,
least-squares regression, least-absolute deviations regression, low-rank
matrix approximation, etc.; numerical and computational issues that arise in
practice in implementing algorithms in different computational environments;
machine learning and statistical issues, as they arise in modern large-scale
data applications; and extensions/connections to related problems as well as
recent work that builds on the basic methods.
Appropriate for advanced graduate students in computer science, statistics, and
mathematics, as well as computationally-inclined students from application
General mathematical sophistication; and a
solid understanding of Algorithms, Linear Algebra, and Probability Theory,
at the advanced undergraduate or beginning graduate level, or equivalent.
This page is a placeholder, since this class is being taught at UC Berkeley.
for the maintained web page for this class, including lectures, etc.