PATTERN RECOGNITION
CSE4PRN
Not currently offered
Credit points: 15
Subject outline
This subject provides students with advanced knowledge in neural networks and related pattern recognition techniques. Main topics include: introduction to pattern recognition; discriminative versus generative approaches; multilayer perceptrons; radial basis function networks; support vector machines; mixture models and the Expectation-Maximization algorithm; Bayesian methods; support vector machines, and Kohonen Networks. Case studies in bioinformatics and document categorization are included. Assignments offer students a sufficiently large space to explore neural networks and related pattern recognition techniques in terms of both theoretical issues and applications in the real world.
School: Engineering and Mathematical Sciences (Pre 2022)
Credit points: 15
Subject Co-ordinator: Andrew Skabar
Available to Study Abroad/Exchange Students: Yes
Subject year level: Year Level 4 - UG/Hons/1st Yr PG
Available as Elective: No
Learning Activities: N/A
Capstone subject: No
Subject particulars
Subject rules
Prerequisites: CSE3ISE (from 2009 CSE3CI)
Co-requisites: N/A
Incompatible subjects: CSE4NN
Equivalent subjects: N/A
Quota Management Strategy: N/A
Quota-conditions or rules: N/A
Special conditions: N/A
Minimum credit point requirement: N/A
Assumed knowledge: 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