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Large Data Methods & Applications (ELEN90094)

Graduate courseworkPoints: 12.5On Campus (Parkville)

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Overview

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Semester 2
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This course provides an introduction to an important contemporary statistical toolset for applications including communication systems, signal processing, financial engineering, data science, machine learning, biomedical engineering and a wealth of other high-dimensional statistical applications. The course will cover topics including introduction to random matrix theory models in engineering; eigenvalue distributions; Wishart and related distributions; finite-dimensional and large-dimensional techniques. These topics will be supplemented by plentiful applications across a broad 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: 10 November 2023