1806 is a linear algebra course at MIT covering matrix theory and vector spaces with emphasis on algorithms and applications.
The course addresses the foundational concepts and computational methods needed to work with linear systems. It begins with vectors and their operations, progressing through matrix multiplication, Gaussian elimination, and more advanced topics including eigenvalues, singular value decomposition, and positive definite matrices. The approach emphasizes practical algorithms and their applications across disciplines, including least-squares approximations, differential equations, networks, Fourier transforms, and Markov processes.
This material suits anyone building mathematical foundations for scientific computing, engineering, or data science work. The course uses linear algebra software and focuses more heavily on matrix algorithms and real-world applications than a more theoretical treatment would. Students should expect to engage with both the mathematical theory and the computational methods needed to solve actual problems.
The repository serves as an archive of course materials organized by semester, with lecture notes, problem sets, and reading assignments posted alongside the official MIT textbook. Previous semesters' materials remain accessible through the repository's branch structure, allowing students to review alternate explanations or additional examples from past offerings.
