Credit points: 15

Subject outline

Quantitative analysis plays an important role in industrial data analytics and knowledge engineering, which makes it very useful to develop computing skills for data regression and classification. This subject covers fundamentals of machine learning techniques in theory and practice. The subject is designed to focus on solving industrial data modelling problems using neural networks. You will learn how to test various learning algorithms and compare performance evaluations. Some advanced machine learning techniques for data classification will also be addressed. You will work with industrial data modelling in labs and assignments to consolidate your knowledge and gain hands-on experience with machine learning applications

SchoolEngineering and Mathematical Sciences

Credit points15

Subject Co-ordinatorPeng Cheng

Available to Study Abroad/Exchange StudentsYes

Subject year levelYear Level 5 - Masters

Available as ElectiveNo

Learning ActivitiesN/A

Capstone subjectNo

Subject particulars

Subject rules

Prerequisites CSE3CI or admission into one of the following courses: SMIT, SMICT,SMITCN SMCSC, SGIT, SGCS or SMDS


Incompatible subjectsCSE3CI OR CSE4CI OR CSE5CI

Equivalent subjectsN/A

Quota Management StrategyN/A

Quota-conditions or rulesN/A

Special conditionsStudents in the following courses are not permitted to enrol: SBCS, SBIT, SBCSGT, SVCSE, SZCSC, SBITP and SBBIY.

Minimum credit point requirementN/A

Assumed knowledgeN/A

Learning resources

Neural Networks and Learning Machines

Resource TypeBook

Resource RequirementRecommended

AuthorSimon Haykin


Edition/Volume3rd edition

PublisherPrentice Hall


Chapter/article titleN/A



Other descriptionN/A

Source locationN/A

Neural Networks and Deep Learning

Resource TypeBook

Resource RequirementRecommended

AuthorMichael Nielsen



PublisherDetermination Press


Chapter/article titleN/A



Other descriptionN/A

Source locationN/A

Career Ready


Work-based learningNo

Self sourced or Uni sourcedN/A

Entire subject or partial subjectN/A

Total hours/days requiredN/A

Location of WBL activity (region)N/A

WBL addtional requirementsN/A

Graduate capabilities & intended learning outcomes

Graduate Capabilities

Intended Learning Outcomes

01. Explain associated concepts and applications of machine learning techniques for data analytics.
02. Critically identify the major components and system design in developing neural networks data regression and classification.
03. Analyse data to design, implement and evaluate machine learning techniques for real world problem solving.
04. Implement a neural network with different learning algorithms for time- series forecasting with real world data from industry.

Subject options

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Start date between: and    Key dates

Melbourne (Bundoora), 2021, Semester 1, Blended


Online enrolmentYes

Maximum enrolment sizeN/A

Subject Instance Co-ordinatorPeng Cheng

Class requirements

Laboratory ClassWeek: 11 - 22
One 2.00 hours laboratory class per week on weekdays during the day from week 11 to week 22 and delivered via face-to-face.

LectureWeek: 10 - 22
One 2.00 hours lecture per week on weekdays during the day from week 10 to week 22 and delivered via face-to-face.


Assessment elementCommentsCategoryContributionHurdle% ILO*

Assignment 1 - Assignment report on neural networks for data modelling, 750 words per student.Group project) (equiv to 750 words per students with maximum 4 students

N/AAssignmentIndividualNo20 SILO4

Assignment 2 - Report presentation- 750 words per student.(Group project) (equiv to 750 words per students with maximum 4 students with maximum 4 students

N/AAssignmentIndividualNo30 SILO4

3 hour Exam (equivalent to 3,000 words)

N/ACentral examIndividualNo50 SILO1, SILO2, SILO3