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Large Data Methods & Applications (ELEN90094)
Graduate courseworkPoints: 12.5On Campus (Parkville)
About this subject
Contact information
Semester 2
Overview
| Availability | Semester 2 |
|---|---|
| Fees | Look 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: 8 November 2024