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

Random Matrix Theory (RMT): A mathematical framework that explores the statistical properties of matrices with random elements, particularly focusing on eigenvalue and eigenvector distributions.

We place ourselves in the setting of high-dimensional statistical inference, where the number of variables p in a data set of interest is of the same order of magnitude as the number of observations n ...

Over the last few issues, we've been talking about the math entity called a matrix. I've given examples of how matrices are useful and how matrix algebra can simplify complicated problems. A messy ...

*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.

Consider two p-variate populations, not necessarily Gaussian, with covariance matrices Σ₁ and Σ₂, respectively. Let S₁ and S₂ be the corresponding sample covariance matrices with degrees of freedom m ...

Random Matrix Theory (RMT) has emerged as an indispensable framework for understanding the statistical properties of matrices whose entries are determined by probabilistic processes. Initially ...