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Module Detailed Information for [EE5907]
Academic Year : 2017/2018 Semester : 1
Correct as at 22 Sep 2018 05:00

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Module Information
Module Code :
Module Title : Pattern Recognition
Module Description : Pattern recognition deals with automated classification, identification, and/or characterizations of signals/data from various sources. The main objectives of this graduate module are to equip students with knowledge of common statistical pattern recognition (PR) algorithms and techniques. Course will contain project-based work involving use of PR algorithms. Upon completion of this module, students will be able to analyze a given pattern recognition problem, and determine which standard technique is applicable, or be able to modify existing algorithms to engineer new algorithms to solve the problem. Topics covered include: Decision theory, Parameter estimation, Density estimation, Non-parametric techniques, Supervised learning, Dimensionality reduction, Linear discriminant functions, Clustering, Unsupervised learning, Feature extraction and Applications.
Module Examinable : -
Exam Date : 24-11-2018 PM
Modular Credits : 4
Pre-requisite : Requires background knowledge such as EE2012 Analytical Methods or CS1101S
Preclusion : EE5907R, EE5026 or EE5027
Module Workload (A-B-C-D-E)* : 2.75-0-0-2-5.25
Remarks : Nil
* A: no. of lecture hours per week
B: no. of tutorial hours per week
C: no. of laboratory hours per week
D: no. of hours for projects, assignments, fieldwork etc per week
E: no. of hours for preparatory work by a student per week

Lecture Time Table
Class TypeWeek TypeWeek DayStartEndRoom

Tutorial Time Table
Attention: The tutorial timetables could be updated from time to time. Students are advised to check regularly for the latest update on the change of tutorial timing.
No Tutorial Class or to be announced. Please check with the department offering this module.

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