1. Handbook
  2. Subjects
  3. Large Data Methods & Applications

Large Data Methods & Applications (ELEN90094)

Graduate courseworkPoints: 12.5Not available in 2026

You’re viewing the 2026 Handbook:
Or view archived Handbooks

Overview

FeesLook up fees

This course provides an introduction to an important contemporary statistical toolset for applications including data science, machine learning, signal processing, financial engineering, biomedical engineering, communication systems and other high-dimensional statistical applications. The course will cover topics including introduction to random matrix theory models in engineering; eigenvalue distributions; finite-dimensional and large-dimensional techniques, covariance estimation, principal component analysis and spectral clustering. These topics will be supplemented by applications across a range of traditional and emerging domains involving big data sets. 

 

Intended learning outcomes

On completion of this subject, students should be able to:

  • Evaluate fundamental theory and advanced concepts of random matrices, including random matrix distributions and techniques, finite and asymptotic;
  • Relate the purpose and application of random matrix methods in the context of broad engineering and data analysis applications;
  • Apply random matrix theory and methods to solve engineering problems;
  • Simulate random matrix models and techniques using software tools.

Generic skills

  • In-depth technical competence in at least one engineering discipline;
  • Ability to apply knowledge of science and engineering fundamentals to undertake problem identification, formulation and solution;
  • Recognition of the role of engineering theories and concepts in addressing interdisciplinary challenges;
  • Expectation of the need to undertake lifelong learning, capacity to do so;
  • Ability to communicate effectively;
  • Capacity for independent critical thought, rational inquiry and self-directed learning.

Last updated: 19 November 2025