Introduction to Machine Learning (COMP90049)
Graduate courseworkPoints: 12.5On Campus (Parkville)
About this subject
Contact information
Semester 1
Hasti Samadi
hasti.samadi@unimelb.edu.au
Semester 2
Lea Frermann
lea.frermann@unimelb.edu.au
Overview
| Availability | Semester 1 Semester 2 |
|---|---|
| Fees | Look up fees |
AIMS
Machine Learning is the study of making accurate, computationally efficient, interpretable and robust inferences from data, often drawing on principles from statistics. This subject aims to introduce students to the intellectual foundations of machine learning, including the mathematical principles of learning from data, algorithms and data structures for machine learning, and practical skills of data analysis.
INDICATIVE CONTENT
Indicative content includes: cleaning and normalising data, supervised learning (classification, regression, linear & non-linear models), and unsupervised learning (clustering), and mathematical foundations for a career in machine learning.
Intended learning outcomes
On completion of this subject students are expected to be able to:
- ILO 1 - Apply elementary mathematical concepts used in machine learning
- ILO 2 - Derive machine learning models from first principles
- ILO 3 - Design, implement, and evaluate machine learning systems for real-world problems
- ILO 4 - Identify the correct machine learning model for a given real-world problem
Generic skills
- On completion of this subject, students should have the following generic skills:
- General skills include the ability to undertake problem identification, formulation, and developing solutions especially exploiting acquired data
- In addition this subject exposes students to use various data processing tools and make them learn integration of these tools to build more complex software systems
- As a result the student will develop skills to utilise a systems approach to complex problems
Last updated: 9 January 2025
Eligibility and requirements
Prerequisites
Students must meet one of the following prerequisite options:
Option 1
One of
| Code | Name | Teaching period | Credit Points |
|---|---|---|---|
| COMP20003 | Algorithms and Data Structures | Semester 2 (On Campus - Parkville) |
12.5 |
| COMP20007 | Design of Algorithms | Semester 1 (On Campus - Parkville) |
12.5 |
| COMP90038 | Algorithms and Complexity |
Semester 1 (On Campus - Parkville)
Semester 2 (On Campus - Parkville)
|
12.5 |
| ENGR30004 | Numerical Algorithms in Engineering |
Semester 2 (On Campus - Parkville)
Semester 1 (On Campus - Parkville)
|
12.5 |
ENGR30003 Numerical Programming for Engineers
Option 2
Admission into or selection of one of the following:
- MC-SCICMP Master of Science (Computer Science)
- MC-CS Master of Computer Science
- GC-CS Graduate Certificate in Computer Science
Option 3
Admission into or selection of one of the following:
- Distributed Computing (100pt) specialisation (formal) in the MC-IT Master of Information Technology
- Computing (100pt) specialisation (formal) in the MC-IT Master of Information Technology
- Cyber Security (100pt) specialisation (formal) in the MC-IT Master of Information Technology
- Artificial Intelligence (100pt) specialisation (formal) in the MC-IT Master of Information Technology
- Human-Computer Interaction (100pt) specialisation (formal) in the MC-IT Master of Information Technology
- Digital Innovation (100pt) specialisation (formal) in the MC-IT Master of Information Technology
Option 4
Admission into or selection of one of the following:
- Distributed Computing (150pt) specialisation (formal) in the MC-IT Master of Information Technology
- Computing (150pt) specialisation (formal) in the MC-IT Master of Information Technology
- Cyber Security (150pt) specialisation (formal) in the MC-IT Master of Information Technology
- Artificial Intelligence (150pt) specialisation (formal) in the MC-IT Master of Information Technology
Corequisites
None
Non-allowed subjects
| Code | Name | Teaching period | Credit Points |
|---|---|---|---|
| COMP30027 | Machine Learning | Semester 1 (On Campus - Parkville) |
12.5 |
COMP30018 Knowledge Technologies
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: 9 January 2025
Assessment
| Description | Timing | Percentage |
|---|---|---|
Programming assignment applying and evaluating specific machine learning method(s) on given dataset(s). Addresses ILOs 3 & 4.
| From Week 4 to Week 7 | 20% |
A final, open-ended (in terms of methodology) research project
| From Week 8 to Week 12 | 30% |
End of semester exam. Closed-book, 2 hours.
| During the examination period | 50% |
Additional details
Last updated: 9 January 2025
Dates & times
- Semester 1
Principal coordinator Hasti Samadi Mode of delivery On Campus (Parkville) Contact hours 36 hours, comprising of two 1-hour lectures and one 1-hour tutorial per week Total time commitment 200 hours Teaching period 26 February 2024 to 26 May 2024 Last self-enrol date 8 March 2024 Census date 3 April 2024 Last date to withdraw without fail 3 May 2024 Assessment period ends 21 June 2024 Semester 1 contact information
Hasti Samadi
hasti.samadi@unimelb.edu.au - Semester 2
Principal coordinator Lea Frermann Mode of delivery On Campus (Parkville) Contact hours 36 hours, comprising of two 1-hour lectures and one 1-hour tutorial per week Total time commitment 200 hours Teaching period 22 July 2024 to 20 October 2024 Last self-enrol date 2 August 2024 Census date 2 September 2024 Last date to withdraw without fail 20 September 2024 Assessment period ends 15 November 2024 Semester 2 contact information
Lea Frermann
lea.frermann@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: 9 January 2025
Further information
- Texts
- Subject notes
LEARNING AND TEACHING METHODS
This course is taught over 12 weeks, each week with two one hour formal lectures and a one hour workshop. During the workshops the students are given problems to solve to reinforce the previous week’s lecturing material. The problem solving nature of the workshops is geared for the students to learn and understand the concepts of the subject material.
INDICATIVE KEY LEARNING RESOURCESChristopher D. Manning, Prabhakar Raghavan, and Hinrich Schutze (2008), Information Retrieval, Cambridge University Press. Freely available at informationretrieval.org
Pang-Ning Tan, Michael Steinbach and Vipin Kumar (2005) Introduction to Data Mining, Addison-Wesley.
CAREERS / INDUSTRY LINKS
This subject is relevant to many fields including Engineering, Commerce, Government Organisations, Research Institutes and Institutions in Medicine where data analysis can play a significant improvement in delivering services or improving profits.
- Related Handbook entries
This subject contributes to the following:
Type Name Course Ph.D.- Engineering Course Doctor of Philosophy - Engineering Course Master of Philosophy - Engineering Course Master of Science (Computer Science) Course Master of Commerce (Decision, Risk and Financial Sciences) Specialisation (formal) Software Major Computer Science Specialisation (formal) Mechatronics Specialisation (formal) Software with Business - 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: 9 January 2025