*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.

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 ...

Vector spaces, linear transformation, matrix representation, inner product spaces, isometries, least squares, generalised inverse, eigen theory, quadratic forms, norms, numerical methods. The fourth ...

Learn how to solve linear systems using the matrix approach in Python. This video explains how matrices represent systems of equations and demonstrates practical solutions using linear algebra ...

The unit will teach some practical aspects of matrix linear algebra that is applicable to engineering problems such as in large-scale data analysis and solution of linear differential equations. By ...

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 ...

Slack for questions about the course and student - led discussions (See Canvas for link) Note about email: Email should be used only for personal/individual matters, and even then it is better to come ...

Linear algebra is rarely described as popular, but rarely does a mathematician portray it in a different, illuminating light. That is certainly one reason Gilbert Strang’s linear algebra lectures are ...