3  5 units
Letter or Credit/No Credit
Introduction to applied linear algebra with emphasis on applications. Vectors, norm, and angle; linear independence and orthonormal sets; applications to document analysis. Clustering and the kmeans algorithm. Matrices, left and right inverses, QR factorization. Leastsquares and model fitting, regularization and crossvalidation. Constrained and nonlinear leastsquares. Applications include timeseries prediction, tomography, optimal control, and portfolio optimization. Undergraduate students should enroll for 5 units, and graduate students should enroll for 3 units. Prerequisites:MATH 51 or CME 100, and basic knowledge of computing (CS 106A is more than enough, and can be taken concurrently). EE103/CME103 and Math 104 cover complementary topics in applied linear algebra. The focus of EE103 is on a few linear algebra concepts, and many applications; the focus of Math 104 is on algorithms and concepts.
GER:DBMath
WAYFR
WAYAQR

Tuesday Thursday 9:00:00 AM  10:20:00 AM @ Hewlett Teaching Center 200 with Eajer Toh Raja Ramesh Trisha Jani Anthony Degleris Logan Spear Sean Chang David Tse Joseph Yen
Tuesday Thursday 12:00:00 PM  1:20:00 PM @ 420041 with Brad Osgood


Tuesday Thursday 9:00:00 AM  10:20:00 AM with Trisha Jani
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Pranav Rajpurkar is a PhD student in Computer Science at Stanford, working on Artificial Intelligence for Healthcare. He was previously a Stanford undergrad ('16).
Brad Girardeau got his B.S, M.S. degrees in computer science at Stanford ('16, '17). When not thinking about computer security, he can be found playing violin or running across the Golden Gate Bridge.
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