printer friendly pageCME 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 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.
Terms: Aut
| Units: 4-5
| UG Reqs: GER:DB-Math, WAY-AQR, WAY-FR
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)
;
Tea-makorn, 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 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.
Terms: Aut
| Units: 3-5
| UG Reqs: GER:DB-Math, WAY-AQR, WAY-FR
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)
;
Tea-makorn, 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, least-squares, 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:DB-Math
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: