Statistical Machine Learning (COMP90051)

Graduate courseworkPoints: 12.5On Campus (Parkville)

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Overview

Availability
Semester 1
Semester 2
FeesLook up fees

AIMS

With exponential increases in the amount of data becoming available in fields such as finance and biology, and on the web, there is an ever-greater need for methods to detect interesting patterns in that data, and classify novel data points based on curated data sets. Learning techniques provide the means to perform this analysis automatically, and in doing so to enhance understanding of general processes or to predict future events.

Topics covered will include: supervised learning, semi-supervised and active learning, unsupervised learning, kernel methods, probabilistic graphical models, classifier combination, neural networks.

This subject is intended to introduce graduate students to machine learning though a mixture of theoretical methods and hands-on practical experience in applying those methods to real-world problems.

INDICATIVE CONTENT

Topics covered will include: linear models, support vector machines, random forests, AdaBoost, stacking, query-by-committee, multiview learning, deep neural networks, un/directed probabilistic graphical models (Bayes nets and Markov random fields), hidden Markov models, principal components analysis, kernel methods.

Intended learning outcomes

On completion of this subject the student is expected to:



  • Describe a range of machine learning algorithms
  • Design, implement and evaluate learning systems to solve real-world problems, based on an appreciation of their relative suitability to different tasks

Generic skills

On completion of the subject students should have the following skills:

  • Ability to undertake problem identification, formulation, and solution
  • Ability to utilise a systems approach to complex problems and to design and operational performance ability to manage information and documentation
  • Capacity for creativity and innovation
  • Ability to communicate effectively both with the engineering team and the community at large

Last updated: 4 March 2025

Eligibility and requirements

Prerequisites

Students must meet one of the following prerequisite options:

Option 1

One of

Code Name Teaching period Credit Points
COMP90049 Introduction to Machine Learning
Semester 2 (On Campus - Parkville)
Semester 1 (On Campus - Parkville)
 12.5
COMP30027 Machine Learning
Semester 1 (On Campus - Parkville)
 12.5
COMP30018 Knowledge Technologies No longer available

Option 2

Admission into the MC-DATASC Master of Data Science

AND

One of

Code Name Teaching period Credit Points
COMP20008 Elements of Data Processing
Semester 1 (On Campus - Parkville)
Semester 2 (On Campus - Parkville)
 12.5
MAST90105 Methods of Mathematical Statistics
Semester 1 (On Campus - Parkville)
 25

Option 3

Admission into or selection of one of the following:

  • 100pt Program course entry point in the MC-IT Master of Information Technology
  • 150pt Program course entry point in the MC-IT Master of Information Technology
  • Distributed Computing specialisation (formal) in the MC-IT Master of Information Technology
  • Computing specialisation (formal) in the MC-IT Master of Information Technology
  • Artificial Intelligence specialisation (formal) in the MC-IT Master of Information Technology

Option 4

Admission into the 100pt Program course entry point in the MC-IT Master of Information Technology

AND

Selection of the Cyber Security specialisation (formal) in the MC-IT Master of Information Technology

Option 5

Admission into the MC-DATASC Master of Data Science

AND

Selection of the Data Science Background Stream (pre-2025)

Admission into the Computational and Statistical Data Science specialisation (formal) in the MC-DATASC Master of Data Science

Corequisites

None

Non-allowed subjects

433-484 Machine Learning

433-679 Evolutionary and Neural Computation

433-680 Machine Learning

433-684 Machine Learning

Recommended background knowledge

Basic probability

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

Two projects due around weeks 7 and 11.

  • 65-70 hours (of work required)
Hurdle requirement: To pass the subject, students must obtain a combined mark of at least 25/50 for the projects.		From Week 7 to Week 1150%

Fortnightly online quizzes, marked based on the best 5 results from 6 quizzes.

  • 15 minutes (each)
Every second week10%

One 2 hour end of semester examination.

  • 2 hours
Hurdle requirement: To pass the subject, students must obtain a mark of at least 20/40 on the exam.		End of semester40%

Additional details

Assessment for this subject addresses both Intended Learning Outcomes (ILOs)

Last updated: 4 March 2025

Dates & times

  • Semester 1
    CoordinatorFeng Liu
    Mode of deliveryOn Campus (Parkville)
    Contact hours36 hours, comprising of two 1-hour lectures and one 1-hour tutorial per week
    Total time commitment200 hours
    Teaching period 3 March 2025 to 1 June 2025
    Last self-enrol date14 March 2025
    Census date31 March 2025
    Last date to withdraw without fail 9 May 2025
    Assessment period ends27 June 2025

    Semester 1 contact information

    Feng Liu
    feng.liu1@unimelb.edu.au

  • Semester 2
    CoordinatorJean Honorio Carrillo
    Mode of deliveryOn Campus (Parkville)
    Contact hours36 hours, comprising of two 1-hour lectures and one 1-hour tutorial per week
    Total time commitment200 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

    Jean Honorio
    jean.honorio@unimelb.edu.au

Time commitment details

200 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

    None

  • Subject notes

    LEARNING AND TEACHING METHODS

    The subject is delivered through a combination of lectures and tutorials. One feature of the subject is that the projects are designed to be relatively open-ended and broad enough that students have scope to get hands-on experience implementing the breadth of material covered in the subject, as well as building off the subject content in innovating their own methods/researching related methods from the research literature and implementing them themselves.

     

    INDICATIVE KEY LEARNING RESOURCES

    Students will have access to lecture slides, readings relating to the lecture materials (both from a textbook and conference/journal papers), tutorial worksheets with worked solutions for all numeric problems, and sample reports to use in writing the project reports. Students are permitted to do their programming in Python and any programming environment/OS.

     

    CAREERS / INDUSTRY LINKS

    Machine learning has been growing rapidly in industry over the past two decades, with key industry players including Google, Microsoft, Amazon and Meta. There have been guest lecturers in the subject from organisations such as NICTA, which has a strong interest in machine learning (indeed one of the primary research groupings within NICTA is based on Machine Learning).

     

  • Related Handbook entries

    This subject contributes to the following:

    TypeName
    CourseMaster of Science (Computer Science)
    CoursePh.D.- Engineering
    CourseMaster of Commerce (Decision, Risk and Financial Sciences)
    CourseMaster of Data Science
    CourseDoctor of Philosophy - Engineering
    CourseMaster of Philosophy - Engineering
  • 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.

    Additional information for this subject

    Subject coordinator approval required.

  • 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