5  1 reviews on Udemy

Linear Algebra

with Applications
Course from Udemy
 18 students enrolled
 en
What are matrices and vectors.
Matrix and vector operations -- addition, subtraction, multiplication, dot product, and transposes.
How to determine if a matrix has an inverse and, if so, how to compute it.
Producing the row echelon form (REF) and reduced row echelon form (RREF) of a matrix.
How to solve systems of equations.
How to find the determinant and rank of a matrix.
What vector spaces and subspaces are.
What is the nullspace and column space of a matrix.
What are linear combinations of vectors, what is the span of a set of vectors, and is the set linearly independent.
What is a basis for a vector space, what are coordinate systems, and how to produce a change of basis.
What is the Invertible Matrix Theorem and why it tells us so much about a matrix.
Orthogonality of vectors, orthogonal projections, orthonormal sets, and orthogonal matrices.
How to perform the Gram-Schmidt orthogonalization process and how to use it to produce the QR factorization of a matrix.
How to find the eigenvalues and eigenvectors of a square matrix.
Less common topics, including least squares, the singular value decomposition, and and introduction to numerical linear algebra.

I believe that linear algebra is the most important area of math that most people have never heard of.  While it has long been important in engineering and the sciences, it is also widely used in the currently popular fields of machine learning and data science.

To give you an idea of how widely it is used, check out the titles of these books:

  • An Introduction to Wavelets Through Linear Algebra

  • Fundamentals and Linear Algebra for the Chemical Engineer

  • Graph Algorithms in the Language of Linear Algebra

  • Intermediate Dynamics: A Linear Algebraic Approach

  • Introduction to Linear Algebra: A Primer for Social Scientists

  • Introduction to Linear Algebra in Geology

  • Introduction to Matrix Methods in Optics

  • Linear Algebra and Optimization for Machine Learning

  • Linear Algebra for Economists

  • Linear Algebra for Signal Processing

  • Matrix Algebra From a Statistician's Perspective

  • Theory of Matrix Structural Analysis

My goal in this course is to introduce you to linear algebra in such a way that you not only understand the purpose of the various topics, but that you also see how you can apply the material.  I hope that if you begin the course thinking "what is linear algebra used for?" that you end the course thinking "what can't you use linear algebra for?"

We will cover standard topics of linear algebra that you can find in any linear algebra textbook, but I also spend a lot of time on topics that are less common in an undergraduate linear algebra course: least squares, singular value decomposition, and numerical linear algebra.

I am a big believer that in order to learn to do something, you have to actually practice doing it.  Therefore, I do the following:

  • work problems by hand, explaining the steps used and promoting understanding of why we are doing it

  • in a few cases the problems are too large or complex to do by hand, so I wrote a computer program in the Python programming language to do the work or plot the values

  • provide practice problems with solutions, showing my work for obtaining the answers

It is also easy to claim that linear algebra is useful but then not back it up. Therefore, for each major topic I include practical applications.

Finally, let me leave you with a quote from Linear Algebra: A Happy Chance to Apply Mathematics by Gilbert Strang, who teaches linear algebra at MIT: "I believe that linear algebra is the most important subject in college mathematics. Isaac Newton would not agree! But he isn't teaching mathematics in the 21st century (and maybe he wasn't a great teacher, we will give him the benefit of the doubt). Certainly Newton demonstrated that the laws of physics are best expressed by differential equations.  He needed calculus: quite right. But the scope of science and engineering and management (and life) is now so much wider, and linear algebra has moved into a central place."

Linear Algebra
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