Fourier and Signal Processing Resources
Professor Strang has published a new textbook that is being used for the current teaching of this course on the MIT campus as well as for Mathematical Methods for Engineers II (18.086). Information about the new book can be found at the Wellesley-Cambridge Press Web site, along with a link to Prof. Strang’s new “Computational Science and Engineering” Web page developed as a resource for everyone learning and doing Computational Science and Engineering.
Signals, Systems, and Control Demonstrations
Automatic Speech Recognition
Linear Algebra Demos
Linear Algebra Java Demos developed by Pavel Grinfeld
Professor Strang’s Related Courses on OCW
Professor Strang’s foundational course 18.06 Linear Algebra has long been one of the most popular courses on OCW. It has received more than 10 million visits since its first publication in 2002. Professor Strang also has a website dedicated to his linear algebra teaching.
A new version of Professor Strang’s classic Linear Algebra was released in 2011 in the innovative OCW Scholar format designed for independent learners. 18.06SC Linear Algebra includes 35 lecture videos and 36 short (and highly-praised) problem-solving help videos by teaching assistants.
Professor Strang has continued to offer new insights into key mathematics subjects. In 2014, he published the new textbook Differential Equations and Linear Algebra. In 2016, that textbook was developed into a series of 55 short videos supported by MathWorks, with parallel videos about numerical solutions by Dr. Cleve Moler, the creator of MATLAB®. The textbook and video lectures help students in a basic ordinary differential equations course. This new series, Learn Differential Equations: Up Close with Gilbert Strang and Cleve Moler, is also available on the MathWorks website.
In 2017, Professor Strang launched a new undergraduate course at MIT: 18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning. Published on the OCW site in 2019, the course uses linear algebra concepts for understanding and creating machine learning algorithms, especially as applied to deep learning and neural networks. This course reviews linear algebra with applications to probability and statistics and optimization and, above all, a full explanation of deep learning.
Professor Raj Rao was the inspiration for the course 18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning, and co-taught it with Professor Strang in the first year it was offered at MIT. Since then, he has developed a very successful course on computational linear algebra and machine learning at the University of Michigan. The website, Mynerva, describes this course, the online textbook, and future plans. Professor Rao’s textbook is complementary to Professor Strang’s new book, Linear Algebra for Everyone.
Professor Strang’s Related Textbooks, Notes, and Videos
Strang, Gilbert. Introduction to Linear Algebra. 5th ed. 2016. Wellesley-Cambridge Press.ISBN: 9780980232776.
Strang, Gilbert. Linear Algebra and Learning from Data. 2019. Wellesley-Cambridge Press. ISBN: 9780692196380.
Strang, Gilbert. Linear Algebra for Everyone. 2020. Wellesley-Cambridge Press. ISBN: 9781733146630.
Strang, Gilbert. Differential Equations and Linear Algebra. 2014. Wellesley-Cambridge Press.ISBN: 9780980232790.
ZoomNotes for Linear Algebra (PDF). Professor Strang created these notes in 2020 and 2021 when many MIT classes were moved online (using Zoom) due to the COVID-19 Pandemic. He hopes that faculty who are planning a linear algebra course and students who are reading for themselves will see these notes.
Guest speaker at the weekly OLSUME (Online Seminar on Undergraduate Mathematics Education) on the topic Linear Algebra and Deep Learning (MP4)
“Linear Algebra, Teaching, and MIT OpenCourseWare” (YouTube) on Lex Fridman Podcast
