Learn methods that can solve real-world problems

What you will learn

• Apply eigenvalues and eigenvectors to solve optimization problems that are subject to distance and orthogonality constraints.
• Model and solve real-world problems using Markov chains, determinants, dynamical systems, and Google Page Rank.
• Apply the iterative Gram Schmidt Process and the QR decomposition to construct an orthogonal basis of a subspace.
• Construct the singular value decomposition (SVD) of a matrix and apply the SVD to estimate the rank and condition number of a matrix, construct a basis for the four fundamental spaces of a matrix, and construct a spectral decomposition of a matrix.
• Apply least-squares and multiple regression to construct a linear model from a data set.

Courses in this program