Analysis of High-Dimensional Data (MAST90110)

Graduate courseworkPoints: 12.5Not available in 2020

You’re viewing the 2020 Handbook:
Or view archived Handbooks
You’re currently viewing the 2020 version of this subject
As part of the University’s response to COVID-19 and the associated Government restrictions and guidelines, most subjects will continue to be delivered online in Winter and Semester 2.
For information about the University’s phased return to campus and in-person activity in Winter and Semester 2, please refer to the on-campus subjects page.

Please refer to the LMS for up-to-date subject information, including assessment and participation requirements, for subjects being offered in 2020.

Overview

FeesLook up fees

Modern data sets are growing in size and complexity due to the astonishing development of data acquisition and storage capabilities. This subject focuses on developing rigorous statistical learning methods that are needed to extract relevant features from large data sets, assess the reliability of the selected features, and obtain accurate inferences and predictions. This subject covers recent methodological developments in this area such as inference for high-dimensional inference regression, empirical Bayes methods, model selection and model combining methods, and post-selection inference methods.

Intended learning outcomes

After completing this subject students should gain:



  • A deeper understanding of statistical methods for high-dimensional data and their properties
  • The ability to apply such methods using a statistical computing package and interpret the results

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

  • problem-solving skills: the ability to engage with unfamiliar problems and identify relevant solution strategies;
  • analytical skills: the ability to construct and express logical arguments and to work in abstract or general terms to increase the clarity and efficiency of analysis;
  • time-management skills: the ability to meet regular deadlines while balancing competing commitments;
  • computer skills: the ability to use statistical computing packages.

Last updated: 12 November 2022

Eligibility and requirements

Prerequisites

Code Name Teaching period Credit Points
MAST90082 Mathematical Statistics
Semester 1 (On Campus - Parkville)
 12.5

AND

One of

Code Name Teaching period Credit Points
MAST30025 Linear Statistical Models
Semester 1 (On Campus - Parkville)
 12.5
MAST90104 A First Course In Statistical Learning
Semester 2 (On Campus - Parkville)
 25

Corequisites

None

Non-allowed subjects

None

Recommended background knowledge

Any of the following:

Code Name Teaching period Credit Points
MAST90085 Multivariate Statistical Techniques
Not available in 2020
 12.5
MAST90083 Computational Statistics & Data Science
Semester 2 (On Campus - Parkville)
 12.5
MAST90084 Statistical Modelling
Semester 1 (On Campus - Parkville)
 12.5

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: 12 November 2022

Assessment

Due to the impact of COVID-19, assessment may differ from that published in the Handbook. Students are reminded to check the subject assessment requirements published in the subject outline on the LMS

DescriptionTimingPercentage

Two written assignments amounting up to 50 pages, due mid and late semester

  • 50 pages
Second half of the teaching period20%

Written examination

  • 3 hours
During the examination period60%

Project

  • 1500 words
End of the teaching period20%

Last updated: 12 November 2022

Dates & times

Not available in 2020

Time commitment details

170 hours

Last updated: 12 November 2022

Further information

  • Texts

    Prescribed texts

    There are no specifically prescribed or recommended texts for this subject.

  • Related Handbook entries

    This subject contributes to the following:

    TypeName
    CourseMaster of Data Science
    CourseMaster of Science (Mathematics and Statistics)
  • 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

    This subject is available to students studying at the University from eligible overseas institutions on exchange and study abroad. Students are required to satisfy any listed requirements, such as pre- and co-requisites, for enrolment in the subject.

Last updated: 12 November 2022