Linear Least Squares | Abeynaya Gnanasekaran
About This Video
The least squares method is one of the most widely used techniques in data science and is used to fit a linear model to data. In this workshop, we will study least squares problems from a linear algebraic perspective and discuss the techniques to solve them.
This workshop assumes that you have a basic understanding of linear algebra including concepts such as matrices, rank, range space, orthogonality, and matrix decompositions (Cholesky, QR, SVD).
This workshop was conducted by Abeynaya Gnanasekaran, a Senior Research Engineer at Raytheon Technologies Research Center.
In This Video
Senior Research Engineer, Raytheon Technologies Research Center
Abeynaya recently graduated with a Ph.D. from ICME (Institute for Computational and Mathematical Engineering) at Stanford and is currently working at the Raytheon Technologies Research Center in Berkeley. At Stanford, she worked with Prof. Eric Darve in developing new, fast, and scalable algorithms for solving sparse linear systems and linear least squares problems. Prior to Stanford, she obtained her Bachelors in Chemical Engineering from IIT Madras, India.