Introduction to Matrix Methods

Course Description

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. 20 students should enroll for 5 units, and 21 students should enroll for 3 units. Prerequisites: MATH 51 orCME 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.

Course Details

Cross-Listings: CME 103

Prerequisites

MATH 51 or CME 100, and basic knowledge of computing ( CS 106A is more than enough, and can be taken concurrently)

Syllabus Link

None available.
Group 3GroupGroup 2