Randomized Algorithms for Matrices and Data Lecture 1 - 09 / 04 / 2013 Lecture 1 : Introduction and Overview

This course will cover recent developments in randomized matrix algorithms of interest in large-scale machine learning and statistical data analysis applications. By this, we will mean basic algorithms for fundamental matrix problems—such as matrix multiplication, least-squares regression, lowrank matrix approximation, and so on—that use randomization in some nontrivial way. This area goes by the name RandNLA (Randomized Numerical Linear Algebra) or RLA (Randomized Linear Algebra). It has led to several rather remarkable theoretical, implementation, and empirical successes so far, and a lot more is currently being developed by researchers.