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- Not Offered

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 k-means algorithm. Matrices, left and right inverses, QR factorization. Least-squares and model fitting, regularization and cross-validation. Constrained and nonlinear least-squares. Applications include time-series prediction, tomography, optimal control, and portfolio optimization. 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:DB-Math

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**Pranav Rajpurkar** aspires to be the next caricature on the wall of CoHo. He enjoys concocting and drinking smoothies, and loves taking photos.

**Brad Girardeau** studies computer science at Stanford. When not thinking about cryptography, he can be found playing violin or running the dish.

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