Machine Learning, Deep Learning, Neural Networks, Programming, Linear Algebra.
to eigendecomposition, where we decompose a matrix into a set of eigenvectors and eigenvalues. Eigenvalues are everywhere! Backing up a little, in real life we encounter matrices and vectors frequently; across a broad interdisciplinary range of subjects the objects we study and the things we can do