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. Undergraduate students should enroll for 5 units, and graduate 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

  • Online Format: Synchronous - This course is taught in real-time, and students are expected to attend virtual sessions at specific times during the week. For more information on the schedule options for this course, please visit the Stanford Explore Courses website.


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

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