M.Sc. in Applied Mathematics, Technion (Israel Institute of Technology) Ph.D. in Applied Mathematics, Caltech (California Institute of Technology) [1] A. Melman (2023): “Matrices whose eigenvalues are ...

Basic linear algebra methods including basic matrix/vector operations, solution of linear systems of equations, eigenvalues, and singular values. Focus will be on applications of the methods on a ...

Introduces linear algebra and matrices with an emphasis on applications, including methods to solve systems of linear algebraic and linear ordinary differential equations. Discusses vector space ...

*Note: This course discription is only applicable to the Computer Science Post-Baccalaureate program. Additionally, students must always refer to course syllabus for the most up to date information.

Sets and maps; vector spaces and linear maps, matrix of linear maps, solving systems of equations, scalar products and orthogonality, eigenvalues and applications. Masters degree credit for Teachers ...

The teaching and learning of linear algebra have evolved significantly over recent decades, underpinned by diverse approaches ranging from theoretical expositions to dynamic, model-based environments.

Understanding and implementation of algorithms to calculate matrix decompositions such as eigenvalue/vector, LU, QR, and SVD decompositions. Applications include data-fitting, image analysis, and ...

This is a subject I struggled with the first time I took it. Ironically, this was the engineering version of it. It wasn't until I took the rigorous, axiomatic version that everything clicked.

This asynchronous online bridge course is specifically designed to help students satisfy the linear algebra admissions requirements for Michigan Tech's Online MS in Applied Statistics, an innovative ...