Random Matrix Theory
Subject MAST90103 (2016)
Note: This is an archived Handbook entry from 2016.
Credit Points:  12.5  

Level:  9 (Graduate/Postgraduate)  
Dates & Locations:  This subject has the following teaching availabilities in 2016: Semester 1, Parkville  Taught on campus.
Timetable can be viewed here. For information about these dates, click here.  
Time Commitment:  Contact Hours: 36 hours consisting of 3 one hour lectures per week Total Time Commitment: 170 hours  
Prerequisites:  Subject Study Period Commencement: Credit Points:  
Corequisites:  None  
Recommended Background Knowledge:  Subject Study Period Commencement: Credit Points:  
Non Allowed Subjects:  None  
Core Participation Requirements: 
For the purposes of considering request for Reasonable Adjustments under the Disability Standards for Education (Cwth 2005), and Student Support and Engagement Policy, academic requirements for this subject are articulated in the Subject Overview, Learning Outcomes, Assessment and Generic Skills sections of this entry. It is University policy to take all reasonable steps to minimise the impact of disability upon academic study, and reasonable adjustments will be made to enhance a student's participation in the University's programs. Students who feel their disability may impact on meeting the requirements of this subject are encouraged to discuss this matter with a Faculty Student Adviser and Student Equity and Disability Support: http://services.unimelb.edu.au/disability 
Subject Overview: 
Random matrix theory is a diverse topic in mathematics. It draws together ideas from linear algebra, multivariate calculus, analysis, probability theory and mathematical physics, amongst other topics. It also enjoys a wide number of applications, ranging from wireless communication in engineering, to quantum chaos in physics, to the Reimann zeta function zeros in pure mathematics. A self contained development of random matrix theory will be undertaken in this course from a mathematical physics viewpoint. Topics to be covered include Jacobians for matrix transformation, matrix ensembles and their eigenvalue probability density functions, equilibrium measures, global and local statistical quantities, determinantal point processes, products of random matrices and Dyson Brownian motion. 

Learning Outcomes: 
After completing this subject students should:

Assessment: 

Prescribed Texts:  None 
Breadth Options:  This subject is not available as a breadth subject. 
Fees Information:  Subject EFTSL, Level, Discipline & Census Date 
Generic Skills: 
In addition to learning specific skills that will assist students in their future careers in science, they will have the opportunity to develop generic skills that will assist them in any future career path. These include:

Related Course(s): 
Master of Science (Mathematics and Statistics) 
Download PDF version.