Two lessons on the LMS algorithm are now available in the Minimum Mean-Square Error Filtering and Least-Squares Problems lesson category. The LMS algorithm is a simple iterative approach to finding a minimum mean-squared error filter or equivalently, solving a least-squares problem. It is widely used in signal processing and related fields and is a powerful tool.
The first lesson Solving Least-Squares Problems with Gradient Descent: the Least Mean-Square Algorithm develops the basic LMS iteration. The second lesson Convergence, Tracking, and the LMS Algorithm Step Size surveys how the performance of the LMS algorithm depends on the step size parameter.