Artificial Intelligence for Engineers (MCEN90048)

Graduate courseworkPoints: 12.5On Campus (Parkville)

You’re viewing the 2023 Handbook:
Or view archived Handbooks
You’re currently viewing the 2023 version of this subject
Undergraduate programs will be delivered on campus. Graduate programs will mainly be delivered on campus, with dual-delivery and online options available to a select number of subjects within some programs.
To learn more, visit 2023 Course and subject delivery.

Overview

Availability
Semester 1
FeesLook 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: 8 November 2024

Eligibility and requirements

Prerequisites

Option 1

Code Name Teaching period Credit Points
MAST20029 Engineering Mathematics
Summer Term (Dual-Delivery - Parkville)
Semester 1 (On Campus - Parkville)
Semester 2 (On Campus - Parkville)
 12.5

AND One of

Code Name Teaching period Credit Points
BMEN20003 Applied Computation in Bioengineering
Semester 1 (On Campus - Parkville)
 12.5
COMP10002 Foundations of Algorithms
Semester 1 (On Campus - Parkville)
Semester 2 (On Campus - Parkville)
 12.5
COMP20005 Intro. to Numerical Computation in C
Semester 2 (On Campus - Parkville)
Semester 1 (On Campus - Parkville)
 12.5
ENGR20005 Numerical Methods in Engineering
Semester 1 (On Campus - Parkville)
Semester 2 (On Campus - Parkville)
 12.5

Option 2

Code Name Teaching period Credit Points
MAST20030 Differential Equations
Semester 2 (On Campus - Parkville)
 12.5

AND

One of

Code Name Teaching period Credit Points
MAST20009 Vector Calculus
Semester 1 (On Campus - Parkville)
Semester 2 (On Campus - Parkville)
 12.5
MAST20032 Vector Calculus: Advanced
Semester 1 (On Campus - Parkville)
 12.5

AND

One of

Code Name Teaching period Credit Points
BMEN20003 Applied Computation in Bioengineering
Semester 1 (On Campus - Parkville)
 12.5
COMP10002 Foundations of Algorithms
Semester 1 (On Campus - Parkville)
Semester 2 (On Campus - Parkville)
 12.5
COMP20005 Intro. to Numerical Computation in C
Semester 2 (On Campus - Parkville)
Semester 1 (On Campus - Parkville)
 12.5
ENGR20005 Numerical Methods in Engineering
Semester 1 (On Campus - Parkville)
Semester 2 (On Campus - Parkville)
 12.5

Corequisites

None

Non-allowed subjects

None

Inherent requirements (core participation requirements)


Last updated: 8 November 2024

Assessment

DescriptionTimingPercentage

Written assignment 1 - Individual programming assignment. Intended Learning Outcomes (ILOs) 1-4 are addressed in this assessment.

  • 8 hours (of work required)
Week 45%

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.

  • 8 hours (of work required)
Week 85%

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.

  • 25 hours (of work required)
Week 1120%

In class tests - pre-lecture quizzes (4 throughout semester) 5 minutes each. ILOs 1-4 are addressed in this assessment.

  • 5 minutes
Throughout the semester10%

End of semester exam - closed book. ILOs 1-4 are addressed in this assessment.

  • 3 hours
Hurdle requirement: Students are required to pass the exam to pass the subject.		During the examination period60%

Last updated: 8 November 2024

Dates & times

  • Semester 1
    Principal coordinatorSaman Halgamuge
    Mode of deliveryOn Campus (Parkville)
    Contact hours36 hours comprising two 1 hour lectures and a 1 hour workshop each week.
    Total time commitment200 hours
    Teaching period27 February 2023 to 28 May 2023
    Last self-enrol date10 March 2023
    Census date31 March 2023
    Last date to withdraw without fail 5 May 2023
    Assessment period ends23 June 2023

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: 8 November 2024

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:

    TypeName
    CourseMaster of Industrial Engineering
    CourseMaster of Engineering
    Specialisation (formal)Mechanical with Aerospace
    Specialisation (formal)Mechanical
    Specialisation (formal)Mechatronics
    Specialisation (formal)Biomedical
  • 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: 8 November 2024