18.065 - Matrix Methods in Data Analysis, Signal Processing, and Machine Learning (Spring 2019)

Class: MWF 1:00 - 2:00

Location: 2-190

Instructor: Gilbert Strang

E-mail: gilstrang@gmail.com

Office: 2-245

Office Hours: before class & more

COURSE DESCRIPTION

Reviews linear algebra with applications to data science and computations: Singular value decomposition, weighted least squares,
signal and image processing, principal component analysis, covariance and correlation matrices, directed and undirected graphs,
matrix factorizations, neural nets, machine learning. Leading to final project not exams.

Prerequisites: 18.06 Linear Algebra

Textbook: Linear Algebra and Learning from Data, by Gilbert Strang (Chapters on Stellar, book 12/2018)

In the first class, Professor Strang will have advice about obtaining the new textbook Linear Algebra and Learning from Data