EE 103: Introduction to Matrix Methods (ENGR 108)
Formerly
EE 103/
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. 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).
ENGR 108 and
Math 104 cover complementary topics in applied linear algebra. The focus of
ENGR 108 is on a few linear algebra concepts, and many applications; the focus of
Math 104 is on algorithms and concepts.
Terms: Sum
| Units: 3-5
| UG Reqs: GER:DB-Math, WAY-AQR, WAY-FR
EE 262: Three-Dimensional Imaging (GEOPHYS 264)
Multidimensional time and frequency representations, generalization of Fourier transform methods to non-Cartesian coordinate systems, Hankel and Abel transforms, line integrals, impulses and sampling, reconstruction tomography, imaging radar. The projection-slice and layergram reconstruction methods as developed in radio interferometry. Radar imaging and backprojection algorithms for 3- and 4-D imaging. In weekly labs students create software to form images using these techniques with actual data. Final project consists of design, analysis and simulation of an advanced imaging system. Prerequisites: None required, but recommend
EE103,
EE261,
EE278, some inverse method concepts such as from
Geophys281.
Terms: Win
| Units: 3
Instructors:
Zebker, H. (PI)
;
Huang, S. (TA)
GEOPHYS 264: Three-Dimensional Imaging (EE 262)
Multidimensional time and frequency representations, generalization of Fourier transform methods to non-Cartesian coordinate systems, Hankel and Abel transforms, line integrals, impulses and sampling, reconstruction tomography, imaging radar. The projection-slice and layergram reconstruction methods as developed in radio interferometry. Radar imaging and backprojection algorithms for 3- and 4-D imaging. In weekly labs students create software to form images using these techniques with actual data. Final project consists of design, analysis and simulation of an advanced imaging system. Prerequisites: None required, but recommend
EE103,
EE261,
EE278, some inverse method concepts such as from
Geophys281.
Terms: Win
| Units: 3
Instructors:
Zebker, H. (PI)
;
Huang, S. (TA)
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