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System Optimisation & Machine Learning (ELEN90088)
Graduate courseworkPoints: 12.5Dual-Delivery (Parkville)
Please refer to the return to campus page for more information on these delivery modes and students who can enrol in each mode based on their location.
About this subject
- Overview
- Eligibility and requirements
- Assessment
- Dates and times
- Further information
- Timetable(opens in new window)
Contact information
Semester 1
Email: tansu.alpcan@unimelb.edu.au
Overview
Availability | Semester 1 - Dual-Delivery |
---|---|
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
- Obtain cognitive, technical and creative skills and an extended understanding to quantitatively analyse and design optimisation and machine learning algorithms for systems engineering.
- Apply fundamental engineering modelling methods to analyse and synthesise optimal and learning systems.
- Obtain 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 analysis tools when appropriate.
- Obtain communication and technical research skills on optimisation and machine learning to justify and interpret related theoretical propositions, methodologies, conclusions and professional decisions to specialist and non-specialist audiences.
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: 3 November 2022
Eligibility and requirements
Prerequisites
Code | Name | Teaching period | Credit Points |
---|---|---|---|
ELEN90054 | Probability and Random Models |
Semester 2 (Dual-Delivery - Parkville)
Semester 1 (Dual-Delivery - Parkville)
|
12.5 |
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.
Inherent requirements (core participation requirements)
Master of Engineering – Electrical, Master of Engineering – Electrical with Business
Last updated: 3 November 2022
Assessment
Semester 1
Description | Timing | Percentage |
---|---|---|
One written examination of three hours at the end of semester. Intended Learning Outcomes (ILOs) 1 to 4 are assessed in the final exam
| End of semester | 60% |
Continuous assessment of submitted project work completed in small groups (2-3 students) in the form of a workshop report, no more than 10 hours. ILOs 3 to 5 are assessed in the group project report
| Week 5 | 9% |
Continuous assessment in the form of multiple online quizzes. ILOs 1, 2, and 4 are assessed in quizzes.
| From Week 3 to Week 11 | 3% |
A one-hour mid-semester test. ILOs 1, 2 & 4 are assessed in this test
| Week 6 | 10% |
Continuous assessment of submitted project work completed in small groups (2-3 students) in the form of a workshop report, no more than 10 hours. ILOs 3 to 5 are assessed in this project report
| Week 8 | 9% |
Continuous assessment of submitted project work completed in small groups (2-3 students) in the form of a workshop report, no more than 10 hours. ILOs 3 to 5 are assessed in this report
| Week 12 | 9% |
Last updated: 3 November 2022
Dates & times
- Semester 1
Principal coordinator Tansu Alpcan Mode of delivery Dual-Delivery (Parkville) Contact hours 200 Total time commitment 200 hours Teaching period 1 March 2021 to 30 May 2021 Last self-enrol date 12 March 2021 Census date 31 March 2021 Last date to withdraw without fail 7 May 2021 Assessment period ends 25 June 2021 Semester 1 contact information
Email: tansu.alpcan@unimelb.edu.au
Last updated: 3 November 2022
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:
Type Name Specialisation (formal) Electrical with Business Specialisation (formal) Electrical - 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