18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning
Reviews linear algebra with applications to life sciences, finance, engineering, 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 computations with large matrices.
This class has 18.06 as a prerequisite.
18.065 will be offered this semester (Spring 2018). It is instructed by G. Strang.
Lecture occurs 1:00 PM to 2:00 PM on Mondays, Wednesdays and Fridays in 2-190.
This class counts for a total of 12 credits.
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