This course is all about matrices, and concisely covers the linear algebra that an engineer should know. We define matrices and how to add and multiply them, and introduce some special types of matrices. We describe the Gaussian elimination algorithm used to solve systems of linear equations and the corresponding LU decomposition of a matrix. We explain the concept of vector spaces and define the main vocabulary of linear algebra. We develop the theory of determinants and use it to solve the eigenvalue problem.
After each video, there are problems to solve and I have tried to choose problems that exemplify the main idea of the lecture. I try to give enough problems for students to solidify their understanding of the material, but not so many that students feel overwhelmed. I do encourage students to attempt the given problems, but if they get stuck, full solutions can be found in my lecture notes.
The mathematics in this matrix algebra course is presented at the level of an advanced high school student, but typically students would take this course after completing a university-level single variable calculus course. There are no derivatives or integrals in this course, but student's are expected to have a certain level of mathematical maturity. Nevertheless, anyone who wants to learn the basics of matrix algebra is welcome to join.
Lecture notes may be downloaded at
Watch the course overview video at
-In this week's lectures, we learn about matrices. Matrices are rectangular arrays of numbers or other mathematical objects and are fundamental to engineering mathematics. We will define matrices and how to add and multiply them, discuss some special matrices such as the identity and zero matrix, learn about transposes and inverses, and define orthogonal and permutation matrices.
SYSTEMS OF LINEAR EQUATIONS
-In this week's lectures, we learn about solving a system of linear equations. A system of linear equations can be written in matrix form, and we can solve using Gaussian elimination. We will learn how to bring a matrix to reduced row echelon form, and how this can be used to compute a matrix inverse. We will also learn how to find the LU decomposition of a matrix, and how to use this decomposition to efficiently solve a system of linear equations.
-In this week's lectures, we learn about vector spaces. A vector space consists of a set of vectors and a set of scalars that is closed under vector addition and scalar multiplication and that satisfies the usual rules of arithmetic. We will learn some of the vocabulary and phrases of linear algebra, such as linear independence, span, basis and dimension. We will learn about the four fundamental subspaces of a matrix, the Gram-Schmidt process, orthogonal projection, and the matrix formulation of the least-squares problem of drawing a straight line to fit noisy data.
EIGENVALUES AND EIGENVECTORS
-In this week's lectures, we will learn about determinants and the eigenvalue problem. We will learn how to compute determinants using a Laplace expansion, the Leibniz formula, or by row or column elimination. We will formulate the eigenvalue problem and learn how to find the eigenvalues and eigenvectors of a matrix. We will learn how to diagonalize a matrix using its eigenvalues and eigenvectors, and how this leads to an easy calculation of a matrix raised to a power.
Learning materials are very organized and each problem always comes with examples. Since I am taking some other courses, the volume is bit larger for me. I wish I get more pair of exercise and solution per topic and ideally this could be 6 weeks. One of highlight is to compute the least square problem (fitting something) using matrix algebra and solving eigenvalue problem. The instructor often mentions about benefit using those algorithm in terms of the efficiency & cost of computation. This is nice indication for me because I'm software engineer who often just "use" existing math libraries, and now I can imagine how they wrote them. I might write my own someday :D
Excellent course, thanks so much! Really like the fact that the videos were backed by a comprehensive set lecture notes with problems AND solutions, including some proofs. This made consuming the concepts much easier. All in all a lot to swallow in this course, but great to get acquainted again (20+ years) with this subject matter. You have an excellent manner of teaching, Jeff. Thank you!
Jeffrey Chasnov is a very charismatic fellow and an outstanding instructor. Lessons were very concise and clutter free. He made a great effort of bringing us engineers (some in formation, some brushing up concepts) the best possible approach for the topics explored. The companion book (the electronic document provided) is the best supplementary material I’ve come across for a MOOC. When someone cares, it shows. It truly shows.
Professor Jeff Chasnov is a great teacher and I hope I had known his course when I first studied matrix at college. He's clear and humorous, and explains the concepts and examples really well. He is the key point that I have committed and finished this course. Thank you Professor Jeff Chasnov!
As someone who has been studying linear algebra independently, this is a great supplement course. There are no field axioms to learn, and vector spaces are VERY generalized. The definition of the determinant is simplified, not like one would find in Georgi E. Shilov's Linear Algebra text.
This course covers most of the important material for applications to differential equations, physics, computer science, economics, etc. Jeff Chasnov does keeps the lessons very tangible, and almost completely avoids abstraction altogether. I highly recommend the course. Even if you have taken an abstract linear algebra course, this is a good way to learn how to apply matrix algebra to real life.
The course understood the needs of science and engineering subject, focus on the necessary part of the theory and skills that is necessary to understand the professions knowledge and literature and presents them in a very clear manner, which is something important that some maths courses trends to forget. The emphasis on the usefulness of matrix actually help different area to think in a clearer way specially the vector space concepts and this actually help students to appreciate the beauty of such theory and will be more willing to learn more harassing maths.
Duccompleted this course, spending 4 hours a week on it and found the course difficulty to be medium.
This was an interesting course, and I learned a lot about matrix algebra. I also took another course of him, which is Fibonacci Numbers and the Golden Ratio. This is the second course of professor Jeffrey Chasnov that I took, and I really like both of his courses. I hope that professor Jeffrey Chasnov will create some more interesting math courses in the future.
Adamcompleted this course, spending 8 hours a week on it and found the course difficulty to be medium.
This is a really good course about Matrix Algebra, there are exercises for particular lectures which really help understand concepts. The quality of videos is high and there are no mistakes in quizzes. I really appreciated it. I would not mind if the course was a bit longer but well everything good ends too quickly.
It was a wonderful refresher of my fundamentals of Vector Algebra. The course is very well designed for online learning. The instructor is very knowledgeable, clear and concise. I would highly recommend the course for all learners - both new and advanced with interest in Mathematical fundamentals underlying Engineering.
Taking this course is paramount important as it is going to enable me to counter some other hard courses such as Physics and Statistics in the near future.It is also going to be an instrumental in pushing me ahead and help me deal with my careers like Geology,Mathematics,Physics and Statistics.