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Artificial Intelligence for Engineers (MCEN90048)
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
Overview
Availability | Semester 1 - Dual-Delivery |
---|---|
Fees | Look up fees |
Upon completion, students are expected to gain an overview of a major area of artificial intelligence known as deep learning, including Convolutional and Recurrent Neural Networks, Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs). Students will also learn computational intelligence methods of optimization and modelling. An ongoing focus will be the applicability of these methods to engineering systems. Students are expected to practice some of the methods they learn on real and synthetic data and appreciate the strengths and limits of the approaches they learn.
A variety of topics in computational intelligence are expected to be covered, with selections to be made from 1) neural networks including generative networks, deep neural networks and convolution neural networks, 2) learning methods including unsupervised learning, reinforcement learning and semi-supervised learning, 3) appreciation of other Computational Intelligence methods: fuzzy systems and evolutionary algorithms and 4) an introduction to stochastic dynamic programming and its relationship to AI. Mechatronic applications in broader terms and case studies from other relevant areas of engineering will be discussed.
Intended learning outcomes
At the conclusion of this subject students should be able to:
- Describe and discuss the concepts of Neural Networks and various types of learning algorithms
- Describe, discuss and apply artificial intelligence approaches to optimization
- Analyse, justify and apply the most appropriate artificial intelligence approaches for various classes of real problems in engineering including computer vision, energy demand forecasting, industrial quality control, engineering design and biomedical engineering
- Implement and analyze the capability and limitations of artificial intelligence in engineering applications
Generic skills
- Application of knowledge of basic science and engineering fundamentals
Last updated: 3 November 2022
Eligibility and requirements
Prerequisites
Code | Name | Teaching period | Credit Points |
---|---|---|---|
MAST20029 | Engineering Mathematics |
Semester 1 (Dual-Delivery - Parkville)
Summer Term (Dual-Delivery - Parkville)
Semester 2 (Dual-Delivery - Parkville)
|
12.5 |
OR
All of
Code | Name | Teaching period | Credit Points |
---|---|---|---|
MAST20009 | Vector Calculus |
Semester 1 (Dual-Delivery - Parkville)
Semester 2 (Dual-Delivery - Parkville)
|
12.5 |
MAST20030 | Differential Equations | Semester 2 (Dual-Delivery - Parkville) |
12.5 |
AND
One of
Code | Name | Teaching period | Credit Points |
---|---|---|---|
BMEN20003 | Applied Computation in Bioengineering | Semester 1 (Dual-Delivery - Parkville) |
12.5 |
COMP10002 | Foundations of Algorithms |
Semester 1 (Dual-Delivery - Parkville)
Semester 2 (Dual-Delivery - Parkville)
|
12.5 |
COMP20005 | Engineering Computation |
Semester 1 (Dual-Delivery - Parkville)
Semester 2 (Dual-Delivery - Parkville)
|
12.5 |
ENGR20005 | Numerical Methods in Engineering |
Semester 1 (Dual-Delivery - Parkville)
Semester 2 (Dual-Delivery - Parkville)
|
12.5 |
Corequisites
None
Non-allowed subjects
None
Inherent requirements (core participation requirements)
Last updated: 3 November 2022
Assessment
Description | Timing | Percentage |
---|---|---|
Written assignment 1 - Individual programming assignment. Intended Learning Outcomes (ILOs) 1-4 are addressed in this assessment.
| Week 4 | 5% |
Written assignment 2 - in a group of 2 -4 students, written analysis on a research or application idea of AI. 8 hours of work per student. ILOs 1-4 are addressed in this assessment.
| Week 8 | 5% |
Written assignment 3 - in a group of 2-4 students, a report and video presentation containing programming and written analysis. ILOs 1-4 are addressed in this assessment.
| Week 11 | 20% |
In class tests - pre-lecture quizzes (4 throughout semester) 5 minutes each. ILOs 1-4 are addressed in this assessment.
| Throughout the semester | 10% |
End of semester exam - closed book. ILOs 1-4 are addressed in this assessment.
| During the examination period | 60% |
Last updated: 3 November 2022
Dates & times
- Semester 1
Principal coordinator Saman Halgamuge Mode of delivery Dual-Delivery (Parkville) Contact hours 36 hours comprising two 1 hour lectures and a 1 hour workshop each week. 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
Last updated: 3 November 2022
Further information
- Texts
Prescribed texts
There are no specifically prescribed or recommended texts for this subject.
- Related Handbook entries
This subject contributes to the following:
Type Name Course Master of Industrial Engineering Course Master of Engineering Specialisation (formal) Biomedical Specialisation (formal) Mechanical Specialisation (formal) Mechatronics Specialisation (formal) Mechanical with Aerospace - 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