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System Optimisation & Machine Learning (ELEN90088)
Graduate courseworkPoints: 12.5On Campus (Parkville)
About this subject
- Overview
- Eligibility and requirements
- Assessment
- Dates and times
- Further information
- Timetable (login required)(opens in new window)
Contact information
Semester 1
Email: tansu.alpcan@unimelb.edu.au
Overview
| Availability | Semester 1 |
|---|---|
| Fees | Look up fees |
This subject introduces the basic principles, analysis methods, and applications of optimisation and machine learning to engineering systems; encompassing fundamental concepts and practical algorithms. It covers the fundamentals of continuous optimisation followed by machine learning basics for engineering applications.
The concepts and methods discussed are illustrated in multiple application areas including Internet of Things (IoT), smart grid and power systems, cyber-security, and communication networks.The concepts taught in this subject will allow a better understanding of continuous optimisation and machine learning for systems engineering.
INDICATIVE CONTENT
Topics covered may include:
- Fundamentals of continuous optimisation: convex sets and functions; local vs global solutions, constrained optimisation and Lagrange multipliers; linear, quadratic, and nonlinear programming
- Basics of machine learning encompassing supervised and unsupervised learning: binary classification, linear and nonlinear regression, kernel methods, and clustering.
- Specific machine learning methods such as Support Vector Machines (SVMs), Neural Networks (NNs), k-means clustering, and reinforcement learning.
- Applications to Internet of Things (IoT), smart grid and power systems, cyber-security, and communication networks.
Intended learning outcomes
On completing this subject it is expected that the student be able to:
- Develop cognitive, technical, and creative skills and an extended understanding to quantitatively analyse and design optimisation and machine learning solutions for systems engineering.
- Apply fundamental engineering modelling methods to analyse and synthesise optimal and learning systems.
- Develop cognitive skills to demonstrate mastery of theoretical knowledge on basic concepts related to system optimisation and machine learning, and their relationship and to reflect critically on their theory and professional practice.
- Apply fundamental techniques from optimisation and machine learning to address problems associated with engineering systems and use numerical and data analysis tools when appropriate while working in a small team.
- Develop communication and technical research skills on optimisation and machine learning to justify and interpret related engineering solutions, methodologies, conclusions, and professional decisions to specialist audiences.
- Communicate effectively with professionals across different engineering disciplines, through media such as concise technical reports and informational videos or live presentations.
Generic skills
On completion of this subject, it is expected that the student will have developed the following generic skills:
- Ability to apply knowledge of basic science and engineering fundamentals
- In-depth technical competence in at least one engineering discipline
- Ability to undertake problem identification, formulation and solution
- Ability to utilise a systems approach to design and operational performance
- Capacity for independent critical thought, rational inquiry and self-directed learning
- Ability to communicate effectively, with the engineering team and with the community at large
Last updated: 16 February 2024
Eligibility and requirements
Prerequisites
None
Corequisites
None
Non-allowed subjects
None
Recommended background knowledge
Medium-level programming skills and knowledge of python programming language are necessary.
Students should have good multi-variable calculus, analytical geometry and linear algebra skills.
Knowledge of the following subject is recommended:
Code
Name
Teaching period
Credit Points
ELEN90054
Probability and Random Models
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: 16 February 2024
Assessment
| Description | Timing | Percentage |
|---|---|---|
A one-hour progress test. ILOs 1, 2 & 3 are assessed in this test
| From Week 4 to Week 6 | 10% |
A one-hour progress test. ILOs 1, 2 & 3 are assessed in this test
| From Week 10 to Week 11 | 10% |
Continuous individual assessment of project work, including peer assessment, not exceeding 50 pages per student over the semester. Approximately 50 hours of work per student. ILOs 1-6 are addressed in this assessment.
| Throughout the teaching period | 55% |
Submission of a final team report not exceeding 30 pages, including an individual contribution statement. Approximately 30 hours of work per team (3-4 students). ILOs 4-6 are addressed in this assessment.
| During the examination period | 25% |
Last updated: 16 February 2024
Dates & times
- Semester 1
Principal coordinator Tansu Alpcan Mode of delivery On Campus (Parkville) Contact hours 36 hours of lectures, 24 hours of workshops 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
Email: tansu.alpcan@unimelb.edu.au
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: 16 February 2024
Further information
- Texts
Prescribed texts
Recommended texts include:
"Convex Optimization" by Stephen Boyd and Lieven Vandenberghe, Cambridge University Press
"Pattern Recognition and Machine Learning" by Christopher Bishop, Springer
"Deep Learning" by Ian Goodfellow and Yoshua Bengio and Aaron Courville, MIT Press
- Related Handbook entries
This subject contributes to the following:
- 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: 16 February 2024