A First Course In Statistical Learning (MAST90104)

Graduate courseworkPoints: 25On Campus (Parkville)

You’re viewing the 2025 Handbook:
Or view archived Handbooks

Overview

Availability
Semester 2
FeesLook up fees

Supervised statistical learning is based on the widely used linear models that model a response as a linear combination of explanatory variables. Initially this subject develops an elegant unified theory for a quantitative response that includes the estimation of model parameters, hypothesis testing using analysis of variance, model selection, diagnostics on model assumptions, and prediction. Some classification methods for qualitative responses are then developed. This subject then considers computational techniques, including the EM algorithm. Bayes methods and Monte-Carlo methods are considered. The subject concludes by considering some unsupervised learning techniques.

Intended learning outcomes

On completion of this subject students should be able to:



  • Understand the underlying statistical theory of linear models and the limitations of such models.
  • Fit linear models to data using a standard statistical computing package and interpret the results.
  • Be able to predict (or classify) qualitative responses.
  • Understand the underlying theory of Bayesian statistics.
  • Understand the underlying theory and be able to apply the EM algorithm in simple settings.
  • Be able to use a computer package to perform statistical computing and data analysis

Last updated: 4 March 2025

Eligibility and requirements

Prerequisites

Code Name Teaching period Credit Points
MAST90105 Methods of Mathematical Statistics
Semester 1 (On Campus - Parkville)
 25

OR

Admission into:

• Graduate Diploma in Data Science - Statistics Stream (GD-DATASC)

Corequisites

None

Non-allowed subjects

Code Name Teaching period Credit Points
MAST30025 Linear Statistical Models
Semester 1 (On Campus - Parkville)
 12.5
MAST30027 Modern Applied Statistics
Semester 2 (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: 4 March 2025

Assessment

DescriptionTimingPercentage

Four written assignments amounting to a total of up to 100 pages. 100pp ≈ 2000 words, due during regular intervals (5% each)

  • 100 pages
During the teaching period20%

Written examination

  • 3 hours
Week 735%

Computer labratory test

  • 45 minutes
End of the teaching period10%

Written examination

  • 3 hours
35%

Last updated: 4 March 2025

Dates & times

  • Semester 2
    CoordinatorWeichang Yu
    Mode of deliveryOn Campus (Parkville)
    Contact hours4 x one hour lectures per week, 1 x one hour practice class per week, and 1 x one hour computer laboratory class per week
    Total time commitment340 hours
    Teaching period28 July 2025 to 26 October 2025
    Last self-enrol date 8 August 2025
    Census date 1 September 2025
    Last date to withdraw without fail26 September 2025
    Assessment period ends21 November 2025

    Semester 2 contact information

    weichang.yu@unimelb.edu.au

Time commitment details

340 hours

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: 4 March 2025

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
  • 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: 4 March 2025