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Machine Learning (COMP30027)
Undergraduate level 3Points: 12.5Dual-Delivery (Parkville)
From 2023 most subjects will be taught on campus only with flexible options limited to a select number of postgraduate programs and individual subjects.
To learn more, visit COVID-19 course and subject delivery.
About this subject
- Overview
- Eligibility and requirements
- Assessment
- Dates and times
- Further information
- Timetable(opens in new window)
Contact information
Semester 1
Overview
| Availability | Semester 1 - Dual-Delivery |
|---|---|
| Fees | Look 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:
- 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:
- Ability at logical problem solving: undertake problem identification, formulation and solution
- Capacity for creativity and innovation
- Ability to communicate effectively within both the engineering team and the community at large
- Profound respect for truth and intellectual integrity, and for the ethics of scholarship
- An expectation of the need to undertake lifelong learning, and the capacity to do so.
Last updated: 18 October 2023
Eligibility and requirements
Prerequisites
All of
| Code | Name | Teaching period | Credit Points |
|---|---|---|---|
| COMP10002 | Foundations of Algorithms |
Semester 2 (Dual-Delivery - Parkville)
Semester 1 (Dual-Delivery - Parkville)
|
12.5 |
| COMP20008 | Elements of Data Processing |
Semester 2 (Dual-Delivery - Parkville)
Semester 1 (Dual-Delivery - Parkville)
|
12.5 |
Corequisites
None
Non-allowed subjects
| Code | Name | Teaching period | Credit Points |
|---|---|---|---|
| ACTL30008 | Actuarial Analytics and Data I | Semester 1 (Dual-Delivery - 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: 18 October 2023
Assessment
| Description | Timing | Percentage |
|---|---|---|
Group or individual project 1 – Programming project (ILO2,5). 30 hours (of work required per student)
| From Week 4 to Week 6 | 20% |
Group or individual project 2 – Prediction competition (ILO1-4). 30 hours (of work required per student)
| From Week 9 to Week 11 | 20% |
Final open book online examination (ILO1-5)
| During the examination period | 60% |
Last updated: 18 October 2023
Dates & times
- Semester 1
Principal coordinator Kris Ehinger Coordinator Ni Ding Mode of delivery Dual-Delivery (Parkville) Contact hours 48 hours total; two 1 hour lectures, one 1 hour tutorial, one 1 hour demonstration, weekly Total time commitment 170 hours Teaching period 28 February 2022 to 29 May 2022 Last self-enrol date 11 March 2022 Census date 31 March 2022 Last date to withdraw without fail 6 May 2022 Assessment period ends 24 June 2022 Semester 1 contact information
Time commitment details
170 hours.
Last updated: 18 October 2023
Further information
- Texts
Prescribed texts
None
- Related Handbook entries
This subject contributes to the following:
Type Name Informal specialisation Science Discipline subjects - new generation B-SCI Major Data Science Major Computer Science Specialisation (formal) Software - Breadth options
This subject is available as breadth in the following courses:
- Bachelor of Commerce
- Bachelor of Fine Arts (Acting)
- Bachelor of Fine Arts (Animation)
- Bachelor of Fine Arts (Dance)
- Bachelor of Fine Arts (Film and Television)
- Bachelor of Fine Arts (Music Theatre)
- Bachelor of Fine Arts (Production)
- Bachelor of Fine Arts (Screenwriting)
- Bachelor of Fine Arts (Theatre)
- Bachelor of Fine Arts (Visual Art)
- 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.
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: 18 October 2023