|Year of offer||2019|
|Subject level||Graduate coursework|
|Fees||Subject EFTSL, Level, Discipline & Census Date|
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.
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.
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
Eligibility and requirements
ELEN90054 Probability and Random Models
Recommended background knowledge
Medium-level programming skills and knowledge of python programming language highly recommended
Core participation requirements
Master of Engineering – Electrical, Master of Engineering – Electrical with Business
|End of semester||60%|
Dates & times
- Semester 1
Principal coordinator Tansu Alpcan Mode of delivery On Campus — Parkville Contact hours 200 Total time commitment 200 hours Teaching period 4 March 2019 to 2 June 2019 Last self-enrol date 15 March 2019 Census date 31 March 2019 Last date to withdraw without fail 10 May 2019 Assessment period ends 28 June 2019
Semester 1 contact information
There are no specifically prescribed or recommended texts for this subject.
- 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.