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Introduction to Machine Learning (COMP90049)
Graduate courseworkPoints: 12.5On Campus (Parkville)
For information about the University’s phased return to campus and in-person activity in Winter and Semester 2, please refer to the on-campus subjects page.
About this subject
- Overview
- Eligibility and requirements
- Assessment
- Dates and times
- Further information
- Timetable(opens in new window)
Contact information
Semester 1
Lea Frermann
Semester 2
Lida Rashidi
Please refer to the LMS for up-to-date subject information, including assessment and participation requirements, for subjects being offered in 2020.
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 the student is expected to:
- Understand elementary mathematical concepts used in machine learning
- Derive machine learning models from first principles
- Design, implement, and evaluate machine learning systems for real-world problems
- 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: 3 November 2022
Eligibility and requirements
Prerequisites
One of the following:
Code | Name | Teaching period | Credit Points |
---|---|---|---|
COMP20003 | Algorithms and Data Structures | Semester 2 (On Campus - Parkville) |
12.5 |
COMP90038 | Algorithms and Complexity |
Semester 1 (On Campus - Parkville)
Semester 2 (On Campus - Parkville)
|
12.5 |
COMP20007 | Design of Algorithms | Semester 1 (On Campus - Parkville) |
12.5 |
ENGR30003 | Numerical Programming for Engineers | Semester 2 (On Campus - Parkville) |
12.5 |
OR
Admission into one of the following courses:
MC-ENG Master of Engineering (200 program only)
MC-IT Master of Information Technology, 100 or 150 pt program in Distributed Computing, Computing, Cyber Security or Artificial Intelligence
MC-IT Master of Information Technology, 100 pt program in Human-Centre Interaction
MC-SCICMP Master of Science (Computer Science)
MC-CS Master of Computer Science
Corequisites
None
Non-allowed subjects
Code | Name | Teaching period | Credit Points |
---|---|---|---|
COMP30018 | Knowledge Technologies | No longer available |
AND
Code | Name | Teaching period | Credit Points |
---|---|---|---|
COMP30027 | Machine Learning | Semester 1 (On Campus - Parkville) |
12.5 |
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: 3 November 2022
Assessment
Due to the impact of COVID-19, assessment may differ from that published in the Handbook. Students are reminded to check the subject assessment requirements published in the subject outline on the LMS
Description | Timing | Percentage |
---|---|---|
End of semester exam.
| During the examination period | 60% |
Individual programming assignment, due in week 5 - 6, requiring approximately 25 - 30 hours.
| From Week 5 to Week 6 | 20% |
Individual programming assignment, due in week 11-12, requiring approximately 25-30 hours.
| From Day 11 to Day 12 | 20% |
Additional details
Last updated: 3 November 2022
Dates & times
- Semester 1
Principal coordinator Lea Frermann Mode of delivery On Campus (Parkville) Contact hours 36 hours, comprising of two 1-hour lectures and one 1-hour workshop per week Total time commitment 200 hours Teaching period 2 March 2020 to 7 June 2020 Last self-enrol date 13 March 2020 Census date 30 April 2020 Last date to withdraw without fail 5 June 2020 Assessment period ends 3 July 2020 Semester 1 contact information
Lea Frermann
- Semester 2
Principal coordinator Lida Rashidi Mode of delivery On Campus (Parkville) Contact hours 36 hours, comprising of two 1-hour lectures and one 1-hour workshop per week Total time commitment 200 hours Teaching period 3 August 2020 to 1 November 2020 Last self-enrol date 14 August 2020 Census date 21 September 2020 Last date to withdraw without fail 16 October 2020 Assessment period ends 27 November 2020 Semester 2 contact information
Lida Rashidi
Time commitment details
200 hours
Last updated: 3 November 2022
Further information
- Texts
Prescribed texts
There are no specifically prescribed or recommended texts for this subject.
- 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 Master of Science (Computer Science) Course Master of Data Science Course Master of Commerce (Decision, Risk and Financial Sciences) Course Doctor of Philosophy - Engineering Course Ph.D.- Engineering Course Master of Philosophy - Engineering Specialisation (formal) Spatial Specialisation (formal) Computing Specialisation (formal) Distributed Computing Major Computer Science Specialisation (formal) Software with Business Specialisation (formal) Software Specialisation (formal) Mechatronics - 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: 3 November 2022