CME 103: Introduction to Matrix Methods (EE 103)
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. 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.
Terms: Aut

Units: 35

UG Reqs: GER:DBMath, WAYAQR, WAYFR

Grading: Letter or Credit/No Credit
Instructors:
Boyd, S. (PI)
;
Angeris, G. (TA)
;
Busseti, E. (TA)
;
Fan, L. (TA)
;
Hwang, J. (TA)
;
Leung, K. (TA)
;
Mu, R. (TA)
;
Nishimura, M. (TA)
;
Park, D. (TA)
;
Pathak, R. (TA)
;
Prasad, V. (TA)
;
Teamangkornpan, P. (TA)
EE 103: Introduction to Matrix Methods (CME 103)
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. 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.
Terms: Aut

Units: 35

UG Reqs: GER:DBMath, WAYAQR, WAYFR

Grading: Letter or Credit/No Credit
Instructors:
Boyd, S. (PI)
;
Angeris, G. (TA)
;
Busseti, E. (TA)
;
Fan, L. (TA)
;
Hwang, J. (TA)
;
Leung, K. (TA)
;
Mu, R. (TA)
;
Nishimura, M. (TA)
;
Park, D. (TA)
;
Pathak, R. (TA)
;
Prasad, V. (TA)
;
Teamangkornpan, P. (TA)
MATH 104: Applied Matrix Theory
Linear algebra for applications in science and engineering: orthogonality, projections, spectral theory for symmetric matrices, the singular value decomposition, the QR decomposition, leastsquares, the condition number of a matrix, algorithms for solving linear systems. (
Math 113 offers a more theoretical treatment of linear algebra.) Prerequisites:
Math 51 and programming experience on par with CS106nnMath 104 and
EE103/CME103 cover complementary topics in applied linear algebra. The focus of
Math 104 is on algorithms and concepts; the focus of EE103 is on a few linear algebra concepts, and many applications.
Terms: Aut, Win

Units: 3

UG Reqs: GER:DBMath

Grading: Letter or Credit/No Credit
Instructors:
Feldheim, O. (PI)
;
Ying, L. (PI)
;
Dore, D. (TA)
;
He, X. (TA)
;
McConnell, S. (TA)
;
Safaee, P. (TA)
;
Velcheva, K. (TA)
;
Wigderson, Y. (TA)
Filter Results: