18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning
Reviews linear algebra with applications to life sciences, finance, and big data. Covers 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, and hidden Markov models.
This class has 18.06 as a prerequisite.
18.065 will not be offered this semester. It will be available in the Spring semester, and will be instructed by G. Strang.
Lecture occurs 11:00 AM to 12:30 PM on Tuesdays and Thursdays in 2-190.
This class counts for a total of 12 credits.
© Copyright 2015 Yasyf Mohamedali