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. 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.
Terms: Aut, Spr

Units: 35

UG Reqs: GER:DBMath, WAYAQR, WAYFR

Grading: Letter or Credit/No Credit
Instructors:
Boyd, S. (PI)
;
Osgood, B. (PI)
;
Tse, D. (PI)
;
Angeris, G. (TA)
;
Chang, S. (TA)
;
Daniel, J. (TA)
;
Degleris, A. (TA)
;
Go, C. (TA)
;
Jani, T. (TA)
;
Jimenez, S. (TA)
;
Kamath, G. (TA)
;
Li, L. (TA)
;
Lin, J. (TA)
;
Nishimura, M. (TA)
;
Patel, N. (TA)
;
Pathak, R. (TA)
;
Sholar, J. (TA)
;
Spear, L. (TA)
CME 263: Introduction to Linear Dynamical Systems (EE 263)
Applied linear algebra and linear dynamical systems with applications to circuits, signal processing, communications, and control systems. Topics: leastsquares approximations of overdetermined equations, and leastnorm solutions of underdetermined equations. Symmetric matrices, matrix norm, and singularvalue decomposition. Eigenvalues, left and right eigenvectors, with dynamical interpretation. Matrix exponential, stability, and asymptotic behavior. Multiinput/multioutput systems, impulse and step matrices; convolution and transfermatrix descriptions. Control, reachability, and state transfer; observability and leastsquares state estimation. Prerequisites: Linear algebra and matrices as in EE103 or
MATH104; ordinary differential equations and Laplace transforms as in EE102B or
CME 102.
Terms: Aut, Sum

Units: 3

Grading: Letter or Credit/No Credit
Instructors:
Nasiri Mahalati, R. (PI)
;
Aboumrad, G. (TA)
;
Chemparathy, A. (TA)
...
more instructors for CME 263 »
Instructors:
Nasiri Mahalati, R. (PI)
;
Aboumrad, G. (TA)
;
Chemparathy, A. (TA)
;
Momeni, A. (TA)
;
Shah, K. (TA)
;
Zhou, Z. (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. 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.
Terms: Aut, Spr

Units: 35

UG Reqs: GER:DBMath, WAYAQR, WAYFR

Grading: Letter or Credit/No Credit
Instructors:
Boyd, S. (PI)
;
Osgood, B. (PI)
;
Tse, D. (PI)
;
Angeris, G. (TA)
;
Chang, S. (TA)
;
Daniel, J. (TA)
;
Degleris, A. (TA)
;
Go, C. (TA)
;
Jani, T. (TA)
;
Jimenez, S. (TA)
;
Kamath, G. (TA)
;
Li, L. (TA)
;
Lin, J. (TA)
;
Nishimura, M. (TA)
;
Patel, N. (TA)
;
Pathak, R. (TA)
;
Sholar, J. (TA)
;
Spear, L. (TA)
EE 263: Introduction to Linear Dynamical Systems (CME 263)
Applied linear algebra and linear dynamical systems with applications to circuits, signal processing, communications, and control systems. Topics: leastsquares approximations of overdetermined equations, and leastnorm solutions of underdetermined equations. Symmetric matrices, matrix norm, and singularvalue decomposition. Eigenvalues, left and right eigenvectors, with dynamical interpretation. Matrix exponential, stability, and asymptotic behavior. Multiinput/multioutput systems, impulse and step matrices; convolution and transfermatrix descriptions. Control, reachability, and state transfer; observability and leastsquares state estimation. Prerequisites: Linear algebra and matrices as in EE103 or
MATH104; ordinary differential equations and Laplace transforms as in EE102B or
CME 102.
Terms: Aut, Sum

Units: 3

Grading: Letter or Credit/No Credit
Instructors:
Nasiri Mahalati, R. (PI)
;
Aboumrad, G. (TA)
;
Chemparathy, A. (TA)
...
more instructors for EE 263 »
Instructors:
Nasiri Mahalati, R. (PI)
;
Aboumrad, G. (TA)
;
Chemparathy, A. (TA)
;
Momeni, A. (TA)
;
Shah, K. (TA)
;
Zhou, Z. (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: Win, Spr

Units: 3

UG Reqs: GER:DBMath

Grading: Letter or Credit/No Credit
Instructors:
Ying, L. (PI)
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