Science & Social Sciences
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Professional Certificate in Applications of Linear Algebra

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Course Features

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Duration

2 months

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Delivery Method

Online

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Available on

Limited Access

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Accessibility

Desktop, Laptop

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Language

English

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Subtitles

English

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Level

Intermediate

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Effort

6 hours per week

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Teaching Type

Self Paced

Course Description

The certificate program covers seven weeks of MATH 1554 Linear Algebra as taught at The Georgia Institute of Technology.

The determinant is the first course. It yields two significant results. You will first be able apply an invertibility criterian to a square matrix, which plays a pivotal part in computer graphics, as well as in advanced courses such multivariable calculus. The second course teaches you how to calculate eigenvalues, eigenvectors, and other concepts. This part of the course aims to dissect the action of any linear transformation that can be visualized. These applications are primarily for discrete dynamical systems including Markov chains. The basic concepts of eigenvectors, eigenvalues, and their applications are applicable to all areas of science, engineering, and industry.

The second course will cover methods for computing approximate solutions to inconsistent systems of equations that do not have solutions. This is a key component in understanding current data science applications. The second course is symmetric matrices. These occur often in applications to the singular value decomposition which is another tool used in data science and machine-learning.

Course Overview

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International Faculty

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Post Course Interactions

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Instructor-Moderated Discussions

Skills You Will Gain

What You Will Learn

Apply eigenvalues and eigenvectors to solve optimization problems that are subject to distance and orthogonality constraints

Apply least-squares and multiple regression to construct a linear model from a data set

Apply the iterative Gram Schmidt Process and the QR decomposition to construct an orthogonal basis of a subspace

Construct the singular value decomposition (SVD) of a matrix and apply the SVD to estimate the rank and condition number of a matrix, construct a basis for the four fundamental spaces of a matrix, and construct a spectral decomposition of a matrix

Model and solve real-world problems using Markov chains, determinants, dynamical systems, and Google Page Rank

Course Instructors

Greg Mayer

Academic Professional in the School of Mathematics

As an Academic Professional in the School of Mathematics at Georgia Tech, I teach undergraduate level courses at the 1000 and 2000 level, support curriculum development initiatives within the School ...
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