Introduces the fundamentals of linear algebra in the context of computer science applications. Includes vector spaces, matrices, linear systems, and eigenvalues.

Computations that involve matrix algorithms are happening everywhere in the world at every moment in time, whether these be embedded in the training of neural networks in data science, in computer ...

This course will cover the fundamental ideas of numerical linear algebra, which, as Trefethen and Bau point out in their preface, is “really applied linear algebra.” They go further: “Numerical linear ...

BS in Computer Science Concentration: ApplicationsLab Science Requirements: 7 Credits Minimum Students must complete two science courses in two different disciplines (BL, CH, KIP, FW, GE, PH, SS) from ...

Harvard offers free courses in multiple subject areas including Arts and Design, Theology, Business, Computer Science, Data Science, Education, Humanities, Programming, Mathematics, Science, and more.

Due to the great ability of tensors to model higher-order data and nonlinear functions, tensor techniques have seen great success in a variety of applications including machine learning, computer ...

The topic of this article is the symmetry analysis of differential equations and the applications of computer algebra to the extensive analytical calculations which are usually involved in it. The ...