Introduction to Numerical Methods
An advanced look at numerical analysis from MIT OpenCourseWare, focused on the accuracy and efficiency of computational algorithms. The course covers sparse-matrix and iterative methods alongside dense-matrix algorithms for solving linear systems and eigenproblems, then works through floating-point arithmetic, backward error analysis, conditioning, and stability. Additional sessions survey numerical integration and nonlinear optimization. Materials include lecture notes, problem sets, and exams drawn from MIT's electrical engineering and computer science curriculum, released under a Creative Commons license for self-paced study. There is no instructor interaction or certificate, but the full set of course materials lets a learner work through the same problems MIT students tackle, with an emphasis throughout on why algorithms succeed or fail numerically rather than just how to code them.