Machine Learning (COMP30027)

Undergraduate level 3Points: 12.5On Campus (Parkville)

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Overview

Availability
Semester 1
FeesLook up fees

AIMS

Machine Learning, a core discipline in data science, is prevalent across Science, Technology, the Social Sciences, and Medicine; it drives many of the products we use daily such as banner ad selection, email spam filtering, and social media newsfeeds. Machine Learning is concerned with making accurate, computationally efficient, interpretable and robust inferences from data. Originally borne out of Artificial Intelligence, Machine Learning has historically been the first to explore more complex prediction models and to emphasise computation, while in the past two decades Machine Learning has grown closer to Statistics gaining firm theoretical footing.

This subject aims to introduce undergraduate students to the intellectual foundations of machine learning, and to introduce practical skills in data analysis that can be applied in graduates' professional careers.

CONTENT

Topics will be selected from: prediction approaches for classification/regression such as k-nearest neighbour, naïve Bayes, discriminative linear models, decision trees, Support Vector Machines, Neural Networks; clustering methods such as k-means, hierarchical clustering; probabilistic approaches; exposure to large-scale learning.

Intended learning outcomes

On completion of this subject the student is expected to:

SILO1. Recognise real-world problems as amenable to machine learning

SILO2. Apply machine learning algorithms and end-to-end statistical processes correctly

SILO3. Interpret the results of machine learning run on real data

SILO4. Compare benefits/drawbacks of competing models and algorithms, relevant to real problems

SILO5. Derive machine learning algorithms from statistical first principles

  • Recognise real-world problems as amenable to machine learning
  • Apply machine learning algorithms and end-to-end statistical processes correctly
  • Interpret the results of machine learning run on real data
  • Compare benefits/drawbacks of competing models and algorithms, relevant to real problems
  • Derive machine learning algorithms from statistical first principles.

Generic skills

On completion of this subject the student is expected to possess:

  1. Ability at logical problem solving: undertake problem identification, formulation and solution
  2. Capacity for creativity and innovation
  3. Ability to communicate effectively within both the engineering team and the community at large
  4. Profound respect for truth and intellectual integrity, and for the ethics of scholarship
  5. An expectation of the need to undertake lifelong learning, and the capacity to do so.

Last updated: 7 March 2025

Eligibility and requirements

Prerequisites

Prerequisites

All of

Code Name Teaching period Credit Points
COMP10002 Foundations of Algorithms
Semester 1 (On Campus - Parkville)
Semester 2 (On Campus - Parkville)
 12.5
COMP20008 Elements of Data Processing
Semester 1 (On Campus - Parkville)
Semester 2 (On Campus - Parkville)
 12.5

OR

Admission into the MC-SOFTENG Master of Software Engineering

Corequisites

None

Non-allowed subjects

Code Name Teaching period Credit Points
COMP90049 Introduction to Machine Learning
Semester 2 (On Campus - Parkville)
Semester 1 (On Campus - Parkville)
 12.5
ACTL30008 Actuarial Analytics and Data I
Semester 1 (On Campus - Parkville)
 12.5

Recommended background knowledge

Basic probability theory, equivalent to material covered in Victorian Certificate of Education (VCE) Mathematical Methods 3/4.

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: 7 March 2025

Assessment

DescriptionTimingPercentage

Assessment / Project - Individual project 1 – Programming project - (ILO2, 3, 5).

  • 20 hours (of work required)
From Week 4 to Week 620%

Group Assessment / Project- Group or individual project 2 – Prediction competition (ILO1-4). 20-25 hours (of work required per student)

  • 20-25 hours (of work required)
From Week 9 to Week 1120%

Final closed-book examination (ILO1-5)

  • 2 hours
During the examination period60%

Last updated: 7 March 2025

Dates & times

  • Semester 1
    Principal coordinatorKris Ehinger
    Mode of deliveryOn Campus (Parkville)
    Contact hours42 hours, comprising two 1-hour lectures and one 1.5-hour tutorial per week. Some computer lab classes will require you to bring your own device.
    Total time commitment170 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

    Kris Ehinger
    kris.ehinger@unimelb.edu.au

Time commitment details

170 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: 7 March 2025

Further information

Last updated: 7 March 2025