Course Description
This course provides a rigorous introduction to fundamentals of random matrix theory motivated by engineering and scientific applications while emphasizing the informed use of modern numerical analysis software. Topics include Matrix Jacobians, Wishart Matrices, Wigner's SemiCircular laws, Matrix beta ensembles, free probability and applications to engineering, science, and numerical computing. Lectures will be supplemented by reading materials and expert guest speakers, emphasizing the breadth of applications that rely on random matrix theory and the current state of the art.
Additional topics will be decided based on the interests of the students. No particular prerequisites are needed though a proficiency in linear algebra and basic probability will be assumed. A familiarity with MATLAB® will also be useful.
Course Objectives
Although applications and analysis using random matrix methods have emerged over the past decade or so, there is a gap between the mathematical theories and understanding of it by engineers. This is primarily because the theory on random matrices, developed almost concurrently by mathematicians, statisticians, and physicists has not yet been as widely used by engineers for there to be a body of literature employing consistent notation in explaining the use of such random matrix based techniques.
Our objective, in this course, is to present the random matrix theories that engineers have successfully used so far in a manner that highlights the historical and intellectual connections between the applications in mathematics, statistics, physics, and engineering. Along the way, we will

Derive the eigenvalue density for sample covariance matrices

Derive Wigner's semicircle law using combinatorial, free probability and resolvent based approaches

Use MATLAB® to develop tests that assess whether a pair of random matrices is asymptotically free

Use the MarcenkoPastur theorem to determine the empirical distribution function for some classes of random sample covariance matrices.
Besides the measurable learning objectives described above, the students will also

Understand the state of the art in the mathematics of finite dimensional random matrices

Understand the fundamental mathematics and intuition for the mathematics of infinite dimensional random matrices including the tools of free probability

Recognize the manner in which these results have been applied so far and be aware of the limitations of these techniques

Use numerical tools such as MATLAB® to understand more difficult open questions in random matrix theory.
Calendar
SES # 
TOPICS 
1 
Introduction 
2 
Matrix Jacobians (the 2x2 case) 
3 
Matrix Jacobians (the 2x2 case) (cont.) 
4 
Matrix Jacobians (the 3x3 case) 
5 
Matrix Jacobians and Spherical Coordinates 
6 
Matrix Jacobians with Wedge Products 
7 
Mechanics of Wedging 
8 
Plucker Coordinates 
9 
Jacobians of Matrix Factorizations 
10 
Jacobians of Matrix Factorizations (cont.) 
11 
Householder Transformations and the Stiefel Manifold 
12 
The CauchyBinet Theorem 
13 
Matrix Jacobians Interpreted Using the CauchyBinet Theorem 
14 
Telatar's Paper and the CauchyBinet Theorem 
15 
Level Densities and the CauchyBinet Theorem 
16 
Orthogonal Polynomials 
17 
Matrix Integrals and Orthogonal Polynomials 
18 
Hypergeometric Functions of Matrix Arguments 
19 
Hypergeometric Functions of Matrix Arguments (cont.) 
20 
Computing Multivariate Orthogonal Polynomials Symbolically 
21 
Random Matrix Theory and Wireless Communications  I 
22 
Random Matrix Theory and Wireless Communications  II 
23 
Random Matrix Theory and Wireless Communications  III 
24 
Projects 
25 
Projects (cont.) 
26 
Projects (cont.) 
27 
Projects (cont.) 
28 
Advances and Open Research Areas in Random Matrix Theory 