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
Eligibility and requirements
Prerequisites
One of
| Code | Name | Teaching period | Credit Points |
|---|---|---|---|
| ELEN90054 | Probability and Random Models | Semester 1 (On Campus - Parkville) |
12.5 |
| COMP90051 | Statistical Machine Learning |
Semester 1 (On Campus - Parkville)
Semester 2 (On Campus - Parkville)
|
12.5 |
| MAST90105 | Methods of Mathematical Statistics | Semester 1 (On Campus - Parkville) |
25 |
Note: these can be taken concurrently (at the same time)
Corequisites
None
Non-allowed subjects
None
Recommended background knowledge
Basic knowledge of probability and linear algebra is expected.
Inherent requirements (core participation requirements)
The University of Melbourne is committed to providing students with reasonable adjustments to assessment and participation under the Disability Standards for Education (2005), and the Assessment and Results Policy (MPF1326). Students are expected to meet the core participation requirements for their course. These can be viewed under Entry and Participation Requirements for the course outlines in the Handbook.
Further details on how to seek academic adjustments can be found on the Student Equity and Disability Support website: http://services.unimelb.edu.au/student-equity/home
Last updated: 8 November 2024
Assessment
| Description | Timing | Percentage |
|---|---|---|
Individual assignment report. Intended Learning Outcomes (ILOs) 1, 3, 4 are addressed in this assessment.
| From Week 4 to Week 5 | 20% |
Individual assignment report. ILOs 1, 3, 4 are addressed in this assessment.
| From Week 9 to Week 10 | 20% |
Submission of a preliminary group-based project report completed in groups (2-3 students). 10 hours of work per student. ILOs 1, 3 are addressed in this assessment.
| Week 11 | 10% |
Submission of a final group report not exceeding 30 pages completed in groups (2-3 students), including an individual contribution statement. 30-40 hours of work per student. ILOs 1, 3 are addressed in this assessment.
| Week 14 | 35% |
Oral presentation of project . 20 mins per group. ILOs 1, are addressed in this assessment.
| Week 14 | 15% |
Last updated: 8 November 2024
Dates & times
- Semester 2
Coordinator Matthew McKay Mode of delivery On Campus (Parkville) Contact hours 24 hours of lectures (1 x two-hour lecture per week) and 12 hours of Tutorials ( 1 x 1 hour Tutorial per week) Total time commitment 200 hours Teaching period 22 July 2024 to 20 October 2024 Last self-enrol date 2 August 2024 Census date 2 September 2024 Last date to withdraw without fail 20 September 2024 Assessment period ends 15 November 2024 Semester 2 contact information
What do these dates mean
Visit this webpage to find out about these key dates, including how they impact on:
- Your tuition fees, academic transcript and statements.
- And for Commonwealth Supported students, your:
- Student Learning Entitlement. This applies to all students enrolled in a Commonwealth Supported Place (CSP).
Subjects withdrawn after the census date (including up to the ‘last day to withdraw without fail’) count toward the Student Learning Entitlement.
Last updated: 8 November 2024
Further information
- Texts
- Subject notes
LEARNING AND TEACHING METHODS
The subject is delivered through lectures and supported by tutorials.
INDICATIVE KEY LEARNING RESOURCES
Students have online access to subject notes, recorded videos, and problem sets with sample solutions. Throughout semester they have access to software such as MATLAB and software programs which are useful for their assignments and the course project.
CAREERS / INDUSTRY LINKS
Exposure to simulation tools and algorithms for processing real-world large data sets, interdisciplinary applications and teamwork, through the workshop-style tutorials and team-based course project. - Related Handbook entries
This subject contributes to the following:
- Available through the Community Access Program
About the Community Access Program (CAP)
This subject is available through the Community Access Program (also called Single Subject Studies) which allows you to enrol in single subjects offered by the University of Melbourne, without the commitment required to complete a whole degree.
Please note Single Subject Studies via Community Access Program is not available to student visa holders or applicants
Entry requirements including prerequisites may apply. Please refer to the CAP applications page for further information.
- Available to Study Abroad and/or Study Exchange Students
Last updated: 8 November 2024