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
School: Engineering and Mathematical Sciences (Pre 2022)
Credit points: 15
Subject Co-ordinator: Nasser Sabar
Available to Study Abroad/Exchange Students: Yes
Subject year level: Year Level 5 - Masters
Available as Elective: No
Learning Activities: N/A
Capstone subject: No
Subject particulars
Subject rules
Prerequisites: CSE3CI or admission into one of the following courses: SMIT, SMICT,SMITCN SMCSC, SGIT, SGCS or SMDS
Co-requisites: N/A
Incompatible subjects: CSE3CI OR CSE5CI OR CSE4CI
Equivalent subjects: N/A
Quota Management Strategy: N/A
Quota-conditions or rules: N/A
Special conditions: Students in the following courses are not permitted to enrol: SBCS, SBIT, SBCSGT, SVCSE, SZCSC, SBITP and SBBIY.
Minimum credit point requirement: N/A
Assumed knowledge: N/A
Learning resources
Neural Networks and Learning Machines
Resource Type: Book
Resource Requirement: Recommended
Author: Simon Haykin
Year: 2009
Edition/Volume: 3rd edition
Publisher: Prentice Hall
ISBN: N/A
Chapter/article title: N/A
Chapter/issue: N/A
URL: http://dai.fmph.uniba.sk/courses/NN/haykin.neural-networks.3ed.2009.pdf
Other description: N/A
Source location: N/A
Neural Networks and Deep Learning
Resource Type: Book
Resource Requirement: Recommended
Author: Michael Nielsen
Year: 2015
Edition/Volume: N/A
Publisher: Determination Press
ISBN: N/A
Chapter/article title: N/A
Chapter/issue: N/A
URL: http://neuralnetworksanddeeplearning.com/
Other description: N/A
Source location: N/A
Career Ready
Career-focused: No
Work-based learning: No
Self sourced or Uni sourced: N/A
Entire subject or partial subject: N/A
Total hours/days required: N/A
Location of WBL activity (region): N/A
WBL addtional requirements: N/A
Graduate capabilities & intended learning outcomes
Graduate Capabilities
Intended Learning Outcomes
Melbourne (Bundoora), 2020, Semester 1, Blended
Overview
Online enrolment: Yes
Maximum enrolment size: N/A
Subject Instance Co-ordinator: Nasser 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 |