EE 500 Linear algebra for EE

  • This is a 1-credit intensive linear algebra course in graduate level.

  • Please review basic math in undergrad level before the first lecture. We usually have a quiz about these basics in the first week.

Lecture notes

Download a handout of linear algebra for EE.

  1. Normed linear space, inner product space.

  2. Block matrix and its inverse; symmetric matrix; quadratic form (x^T A x); positive definite matrix; Schur complement

  3. Eigenvalue and important properties of some common matrices

  4. Matrix decompositions: LU, SVD, Cholesky; a review on solving linear equations

  5. Multivariate calculus; gradient, Hessian; solving nonlinear equations (bisection, Newton)

References: texbooks, class notes

Textbooks on linear algebra with applications

    1. Boyd and L. Vandenberghe, Introduction to Applied Linear Algebra: Vectors, Matrices, and Least squares, Cambridge, 2018

    1. Strang, Linear Algebra and Learning from Data, Wellesley-Cambridge Press, 2019

  3. C.C. Aggrawal, Linear algebra and Optimization for Machine Learning: A Texbook, Springer 2020

  4. R.A. Horn and C.R. Johnson, Matrix Analaysis, 2nd Edition, Cambridge, 2012

  5. M.P. Deisenroth, A.A. Faisal, and C.S. Ong, Mathematics for Machine Learning, Cambridge University Press, 2020

Textbooks on mathematical analysis

  1. A.N. Kolmogorov and S.V. Fomin, Introductory real analysis, Dover, 1970