Computational Science and Engineering I
Gilbert Strang's MIT course builds the core toolbox for solving science and engineering problems computationally. It opens with a review of linear algebra and its applications to networks, structures, and estimation, then moves into finite difference and finite element methods for differential equations, Laplace's equation and potential flow, boundary-value problems, Fourier series, the discrete Fourier transform, and convolution. Later sessions connect these classical tools to topics in AI and machine learning. Materials include full video lectures, lecture notes, problem sets, and exams published through MIT OpenCourseWare, free to access with no enrollment or certificate cost. The course assumes prior exposure to linear algebra and differential equations and suits students who want the mathematical machinery behind simulation and modeling rather than a purely theoretical treatment.