Machine Learning (COMP30027)
Undergraduate level 3Points: 12.5On Campus (Parkville)
Overview
| Availability | Semester 1 |
|---|---|
| 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
INTENDED LEARNING OUTCOMES (ILO)
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: 4 April 2025
Eligibility and requirements
Prerequisites
Both of:
| Code | Name | Teaching period | Credit Points |
|---|---|---|---|
| COMP20008 | Elements of Data Processing |
Semester 1 (On Campus - Parkville)
Semester 2 (On Campus - Parkville)
|
12.5 |
| COMP10002 | Foundations of Algorithms |
Semester 2 (On Campus - Parkville)
Semester 1 (On Campus - Parkville)
|
12.5 |
Corequisites
None
Non-allowed subjects
None
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: 4 April 2025
Assessment
Additional details
- Group-work project 1 – Programming project (ILO2,5), requiring approximately 30 hours of work, due in weeks 5-6 (20%)
- Group-work project 2 – Prediction competition (ILO1-4, requiring approximately 30 hours of work, due in weeks 9-10 (20%)
- Final 2 hours, closed book examination (ILO1-5), during the examination period (60%)
Last updated: 4 April 2025
Dates & times
- Semester 1
Coordinator Jeremy Nicholson Mode of delivery On Campus (Parkville) Contact hours 48 hours total; two 1 hour lectures, one 1 hour practice class, one 1 hour tutorial, weekly. Total time commitment 170 hours Teaching period 4 March 2019 to 2 June 2019 Last self-enrol date 15 March 2019 Census date 31 March 2019 Last date to withdraw without fail 10 May 2019 Assessment period ends 28 June 2019 Semester 1 contact information
Jeremy Nicholson
Time commitment details
170 hours.
Last updated: 4 April 2025
Further information
- Texts
- Related Handbook entries
This subject contributes to the following:
Type Name Informal specialisation Science-credited subjects - new generation B-SCI Major Data Science Informal specialisation Selective subjects for B-BMED Specialisation (formal) Software Major Computer Science - Breadth options
- 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
Last updated: 4 April 2025