cse5ml machine learning
MACHINE LEARNING
CSE5ML
2020
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-ordinatorNasser Sabar
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
Co-requisitesN/A
Incompatible subjectsCSE3CI OR CSE5CI OR CSE4CI
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
Year2009
Edition/Volume3rd edition
PublisherPrentice Hall
ISBNN/A
Chapter/article titleN/A
Chapter/issueN/A
URLhttp://dai.fmph.uniba.sk/courses/NN/haykin.neural-networks.3ed.2009.pdf
Other descriptionN/A
Source locationN/A
Neural Networks and Deep Learning
Resource TypeBook
Resource RequirementRecommended
AuthorMichael Nielsen
Year2015
Edition/VolumeN/A
PublisherDetermination Press
ISBNN/A
Chapter/article titleN/A
Chapter/issueN/A
URLhttp://neuralnetworksanddeeplearning.com/
Other descriptionN/A
Source locationN/A
Career Ready
Career-focusedNo
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
Subject options
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Melbourne (Bundoora), 2020, Semester 1, Blended
Overview
Online enrolmentYes
Maximum enrolment sizeN/A
Subject Instance Co-ordinatorNasser Sabar
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.
Assessments
Assessment element | Category | Contribution | Hurdle | % | 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/A | N/A | No | 20 | 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/A | N/A | No | 30 | SILO4 |
3 hour Exam (equivalent to 3,000 words) | N/A | N/A | No | 50 | SILO1, SILO2, SILO3 |