Linear Algebra  Foundations to Frontiers (LAFF)  University of Texas at Austin
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Informazione importanti
 Corso
 Online
 Quando:
Da definire
Learn the mathematics behind linear algebra and link it to matrix software development.
With an apprenticeship you earn while you learn, you gain recognized qualifications, job specific skills and knowledge and this helps you stand out in the job market.With this course you earn while you learn, you gain recognized qualifications, job specific skills and knowledge and this helps you stand out in the job market.
Requisiti: High School Algebra, Geometry, and PreCalculus.
SediInizio  Luogo 

Da definire 
Online

Cosa impari in questo corso?
Algebra  Math  Linear Algebra  LAFF  
Foundations to Frontiers 
Programma
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Foundations to Frontiers (LAFF) is packed full of challenging, rewarding material that is essential for mathematicians, engineers, scientists, and anyone working with large datasets. Students appreciate our unique approach to teaching linear algebra because:
 It’s visual.
 It connects hand calculations, mathematical abstractions, and computer programming.
 It illustrates the development of mathematical theory.
 It’s applicable.
In this course, you will learn all the standard topics that are taught in typical undergraduate linear algebra courses all over the world, but using our unique method, you'll also get more! LAFF was developed following the syllabus of an introductory linear algebra course at The University of Texas at Austin taught by Professor Robert van de Geijn, an expert on high performance linear algebra libraries. Through short videos, exercises, visualizations, and programming assignments, you will study Vector and Matrix Operations, Linear Transformations, Solving Systems of Equations, Vector Spaces, Linear LeastSquares, and Eigenvalues and Eigenvectors. In addition, you will get a glimpse of cutting edge research on the development of linear algebra libraries, which are used throughout computational science.
 The connection between linear transformations, matrices, and systems of linear equations
 Partitioning methods and special characteristics of triangular, symmetric, diagonal, and invertible matrices
 A variety of algorithms for matrix and vector operations and for solving systems of equations
 Vector spaces, subspaces, and various characterizations of linear independence
 Orthogonality, linear leastsquares, projections, bases, and low rank approximations
 Eigenvalues and eigenvectors
 How to create a small library of basic linear algebra functions