Applications of Linear Algebra Part 2 - Davidson CollegeedX
What you'll learn on the course
Our world is in a data deluge with ever increasing sizes of datasets. Linear algebra is a tool to manage and analyze such data. This course is part 2 of a 2-part course, with this part extending smoothly from the first. Note, however, that part 1, is not a prerequisite for part 2. In this part of the course, we'll develop the linear algebra more fully than part 1. This class has a focus on data mining with some applications of computer graphics. We'll discuss, in further depth than part 1, sports ranking and ways to rate teams from thousands of games. We’ll apply the methods to March Madness. We'll also learn methods behind web search, utilized by such companies as Google. We'll also learn to cluster data to find similar groups and also how to compress images to lower the amount of storage used to store them. The tools that we learn can be applied to applications of your interest. For instance, clustering data to find similar movies can be applied to find similar songs or friends. So, come to this course ready to investigate your own ideas.
Tim Chartier Professor, Department of Mathematics and Computer Science Associate Professor of Mathematics and Computer Science at Davidson College, Dr. Tim Chartier specializes in applied linear algebra in the fields of data analytics and partial differential equations. In January 2014, he was named the inaugural Math Ambassador for the Mathematical Association of America, an organization that also recognized Dr. Chartier's ability to communicate math with a national teaching award. His research and scholarship were recognized with an Alfred P. Sloan Research Fellowship. Published by Princeton University Press, Tim wrote Math Bytes: Google Bombs, Chocolate-Covered Pi, and Other Cool Bits in Computing and coauthored the textbook Numerical Methods: Design, Analysis, and Computer Implementation of Algorithms.