NUS
 
ISS
 
NUS ISS

Practice Module for Pattern Recognition Systems

Overview

Part of Graduate Certificate in Pattern Recognition Systems
Duration 10 days
Course Time
Enquiry Please contact ask-iss@nus.edu.sg for more details.

Please indicate your interest at this link

The goal of the Graduate Certificate in Pattern Recognition Systems (PRS) is to teach participants the skills and industry best practices for developing pattern recognition systems.

This certificate provides a well-rounded foundation of knowledge and concepts in pattern recognition, machine learning, deep learning, intelligent sensing and sense making. This certificate consists of three component courses and a practice module.

 

Objectives

  • Firstly, to expose participants to real world problems so that they may practice the use of the knowledge and skills they have learned during the component courses in a coherent manner.
  • Secondly, to enable participants to demonstrate their proficiency across all of the skills that they have learned in the course modules and hence be certified as competent at the Certificate level.


Intended Audience

This practice module is targeted at participants who wish to complete the certification process for the Certificate in Pattern Recognition Systems.



Prerequisites

Participants must have successfully completed all (or have been exempted from) the three course modules for the Certificate in Pattern Recognition Systems:

  1. Problem Solving using Pattern Recognition
  2. Pattern Recognition and Machine Learning Systems
  3. Intelligent Sensing and Sense Making


Components

There are two parts to the Practice Module.
  • Practice Project: Participants will need to undertake a project to gain practical experience and demonstrate their understanding and mastery of the skills taught in the three component courses. The practice project will require each participant to expend an estimated 10 days of effort. These days are not expected to be contiguous and may stretch over many weeks. These projects may be conducted by individual participants or in teams depending on the nature of the project requirements.
  • Examination: Each participant is required to sit for an examination on a stipulated date and time.

    • The overall grade for the participant will be based on the Practice Project and Examination.

    Practice Project Examples:
    • House price prediction
      • Estimate house price
      • Recommend neighborhood of property
      • Use deep neural networks, clustering and dimensionality reduction techniques
    • Image Based Traffic Density Real Time Prediction
      • Real-time traffic image and prediction
      • Image based traffic density method
      • Deep neural networks
    • Automatic image caption generation
    • Wearable sensors for human activity recognition
    • Hand gesture recognition


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