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Module Detailed Information for [EE5904]
Academic Year : 2017/2018 Semester : 2
Correct as at 30 Mar 2018 04:27

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Module Information
Module Code :
Module Description : In this module students will learn various neural network models and develop all the essential background needed to apply these models to solve practical pattern recognition and regression problems. The main topics that will be covered are: single and multilayer perceptrons, support vector machines, radial basis function networks, Kohonen networks, principal component analysis, and recurrent networks. There is a compulsory computer project for this module. This module is intended for graduate students and engineers interested in learning about neural networks and using them to solve real world problems.
Module Examinable : -
Exam Date : 09-05-2018 AM
Modular Credits : 4
Pre-requisite : Nil
Preclusion : ME5404, EE5904R, MCH5202
Module Workload (A-B-C-D-E)* : 3-0-0-3-4
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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