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Pattern Classification - COMP9515
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ENG/Comp1
 
Faculty: Faculty of Engineering
 
 
School:  School of Computer Science and Engineering
 
 
Campus: Kensington Campus
 
 
Career: Postgraduate
 
 
Units of Credit: 6
 
 
EFTSL: 0.125 (more info)
 
 
Contact Hours per Week: 3
 
 
Enrolment Requirements:
 
 
Prerequisite: COMP9024 or COMP2011 or COMP2711 or COMP2091 (or extended versions) or enrolment in MIT program 8684 or enrolment in GradCert program 7344.
 
 
Session Offered: See Class Timetable
 
 
Fee Band: 2 (more info)
 
 

Description

The course has three basic aims: firstly to understand the field of pattern recognition in general, secondly to get familiar with pattern recognition techniques, and thirdly to obtain the ability to apply techniques to applications.

This course is an introduction to the subject of pattern recognition. We will cover theoretical foundations of classification and pattern recognition and discuss applications in character, speech, and other applications. A tentative list of topics includes: Bayesian decision theory, discriminate functions for normal class distributions, supervised learning, unsupervised learning and clustering, Structural and Syntactic pattern recognition, Edit distance, String matching, Statistical pattern recognition, and neural pattern recognition.
Assumed Knowledge for PG: MATH2859 or MATH2801 or MATH2901 or statistical course equivalent.

Further Information

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