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Introduction to Machine Learning (COMP90049)
Graduate courseworkPoints: 12.5Dual-Delivery (Parkville)
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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
Email: rashidi.l@unimelb.edu.au
Overview
Availability | Semester 1 - Dual-Delivery Semester 2 - Dual-Delivery |
---|---|
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 - Understand 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: 31 January 2024
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 (Dual-Delivery - Parkville) |
12.5 |
COMP20007 | Design of Algorithms | Semester 1 (Dual-Delivery - Parkville) |
12.5 |
COMP90038 | Algorithms and Complexity |
Semester 1 (Dual-Delivery - Parkville)
Semester 2 (Dual-Delivery - Parkville)
|
12.5 |
ENGR30004 | Numerical Algorithms in Engineering |
Semester 1 (Dual-Delivery - Parkville)
Semester 2 (Dual-Delivery - Parkville)
|
12.5 |
ENGR30003 Numerical Programming for Engineers
Option 2
Admission into 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 one of the following: 100pt Program course entry point in the MC-IT Master of Information Technology, 150pt Program course entry point in the MC-IT Master of Information Technology
AND
Selection of one of the following:
- Distributed Computing specialisation (formal) in the MC-IT Master of Information Technology
- Computing specialisation (formal) in the MC-IT Master of Information Technology
- Cyber Security specialisation (formal) in the MC-IT Master of Information Technology
- Artificial Intelligence specialisation (formal) in the MC-IT Master of Information Technology
Option 4
Admission into the 100pt Program course entry point in the MC-IT Master of Information Technology
AND
Selection of the Human-Computer Interaction 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 (Dual-Delivery - Parkville) |
12.5 |
COMP30018 Knowledge Technologies
433-352 Data on the Web
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: 31 January 2024
Assessment
Description | Timing | Percentage |
---|---|---|
Two short programming assignments.
| From Week 2 to Week 8 | 30% |
A final, open-ended (in terms of methodology) research project
| From Week 11 to Week 12 | 30% |
End of semester exam. Online, closed-book, 2 hours.
| During the examination period | 40% |
Additional details
Intended Learning Outcomes (ILOs) 1 and 2 are addressed in the lectures and workshops; ILOs 3 and 4 are addressed in the assignments and project; ILOs 1--4 are addressed in the exam.
Last updated: 31 January 2024
Dates & times
- Semester 1
Principal coordinator Lea Frermann Mode of delivery Dual-Delivery (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 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
Lea Frermann
- Semester 2
Principal coordinator Lida Rashidi Mode of delivery Dual-Delivery (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 25 July 2022 to 23 October 2022 Last self-enrol date 5 August 2022 Census date 31 August 2022 Last date to withdraw without fail 23 September 2022 Assessment period ends 18 November 2022 Semester 2 contact information
Email: rashidi.l@unimelb.edu.au
Time commitment details
200 hours
Last updated: 31 January 2024
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 Commerce (Decision, Risk and Financial Sciences) Course Ph.D.- Engineering Course Doctor of Philosophy - Engineering Course Master of Science (Computer Science) Course Master of Philosophy - Engineering Specialisation (formal) Computing Specialisation (formal) Distributed Computing Specialisation (formal) Software with Business Specialisation (formal) Software Specialisation (formal) Mechatronics Major Computer Science - 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: 31 January 2024