ECE@NUS
ECE@NUS

Graduate Certificates

Graduate Certificates

 


Overview

The Graduate Certificate (GC) Programme on Internet of Things (IoT) is hosted by the Department of Electrical and Computer Engineering. These are bite-size coursework based programmes structured around lectures and end-of-module examinations.

Some of the modules will be held during the regular semester I and II of the academic year, and these are usually held on weekdays in the evening from 6pm to 9pm. Otherwise, classes may be held during daytime, including Saturdays. Subject to appropriate mutual arrangements and student numbers, corporate classes may also be arranged.

The programmes provide opportunities for working professionals, practicing engineers and university graduates to upgrade their knowledge and core capabilities in various area of engineering related to these GCs.

Graduate Certificates will be awarded to candidates who have taken the required set of modules successfully with a CAP (Cumulative Average grade Point) of at least 2.5.

The modules offered under the GC programmes may also be taken individually, in which case a Certificate of Participation will be offered to those who have completed the module satisfactorily.

The current fee for each module is $2900 (subject to GST). Singapore Citizens and Permanent Residents are entitled to course fee subsidies under the SSG (Skills Future) for modules that are approved by SSG. These subsidies are all subject to SSG’s terms and conditions.



Programme Requirements

Admission The programme is open to graduates with relevant undergraduate degree and/or work experience.
Award of Graduate Certificate

A Graduate Certificate will be awarded to candidates who obtained the stipulated number of MCs (Module Credits, with each module having 2MCs) with a CAP (Cumulative Average grade Point) of at least 2.5 (out of a range of 0 to 5) within a maximum candidature of 3 years.

The requirement for the IoT programme is 16 modular credits (16 MCs).

Award of Certificate of Participation Candidates who do not take examinations or fail to attain a CAP of at least 2.5 will be given a Certificate of Participation listing those modules which have been taken with at least 70% attendance.
Per Module Contact Hours The contact hours for a 2-MC module will be between 18 – 20 hours in total, which include lectures, quizzes, projects and assessments.


Internet of Things (IoT)

Under the GC Programme on IoT, the following modules are offered. Each of these modules carries 2MC, and has about 18 to 20 formal contact hours (dependent on the extend of online or preparation materials).

List of Modules (Note: Not all modules are offered in each semester)

Module Class Schedule / Lecture Session Outline
EE5020 Data Science for Internet of Things
Class Schedule
AY18/19
Semester 2

Lecture Sessions
Lecture Saturday 23 Feb 2019, 1:00-4:00pm
Lecture Monday 25 Feb 2019: 6:00-9:00pm
Lecture Tuesday 26 Feb 2019: 6:00-9:00pm
Lecture Wednesday 27 Feb 2019: 6:00-9:00pm
Lecture Thursday 28 Feb 2019: 6:00-9:00pm
Lecture Saturday 2 Mar 2019: 1:00-4:00pm
Assessment Saturday 9 Mar 2019: 1:00-3:00pm (2hr) 

Lectured by
Assoc. Prof Mehul Motani
This module covers data science for the Internet of Things. The topics include data science fundamentals such as Bayesian statistics, classification, supervised learning, unsupervised learning, and deep learning. The module will also cover several basic machine learning algorithms such as decision trees, logistic regression, support vector machines, and neural networks. Students will visualize and analyze real-world data sets via practical IoT case studies.
EE5021 Cloud Based Services for Internet of Things
Class Schedule
Semester 2 
AY18/19

Lecture Session
Lecture Thursday 7 March 2019: 6:00-9:00pm
Lecture Thursday 14 March 2019: 6:00-9:00pm
Lecture Thursday 21 March 2019: 6:00-9:00pm
Lecture Thursday 28 March 2019: 6:00-9:00pm
Lecture Thursday 4 April 2019: 6:00-9:00pm
Lecture Thursday 11 April 2019: 6:00-9:00pm
Assessment Thursday 18 April 2019: 6:00-8:00pm (2hr)

Lectured by
Assoc. Prof Mohan Gurusamy
Cloud computing is an attractive paradigm for cost efficiency and management flexibility which brings in several benefits for Internet of Things (IoT). This module provides a comprehensive treatment of the concept and techniques related to cloud-based services for IoT applications. It first briefly reviews IoT basics and then discusses cloud computing and request models that can be used for IoT. It also introduces network function virtualization (NFV), orchestration, and IoT Gateway. Tutorial and hands-on will be provided to the students to acquire practical experience in working with cloud platforms and use/test the cloud-based services for IoT applications.
EE5022 Cyber Security for Internet of Things
Class Schedule
AY18/19
Semester 2

Lecture Sessions
Lecture Saturday 20 Apr 2019, 1:00-5:00pm
Lecture Monday 22 Apr 2019: 6:00-9:00pm
Lecture Tuesday 23 Apr 2019: 6:00-9:00pm
Lecture Wednesday 24 Apr 2019: 6:00-9:00pm
Lecture Thursday 25 Apr 2019: 6:00-9:00pm
Lecture Friday 26 Apr 2019: 6:00-9:00pm
Assessment Saturday 4 May 2019: 2:00-4:00pm(2hr)
 
Lectured by 
Assoc. Prof Biplab Sikdar 
This module will introduce tools and methodologies for cyber security of IoT systems. The topics covered include the basic of cyber security for IoT systems, threats and vulnerabilities, tools and techniques for detecting attacks, and mitigation strategies. In addition to the fundamental concepts, students will be exposed to hands on training for analyzing IoT and cyber-physical system data for detecting cyber-threats and attacks.
EE5023 Wireless Networks
Class Schedule 
AY18/19
Semester 2

Lecture Sessions
Lecture Thursday 17 Jan 2019: 6:00-9:00pm
Lecture Thursday 24 Jan 2019: 6:00-9:00pm
Lecture Thursday 31 Jan 2019: 6:00-9:00pm
Lecture Thursday 7 Feb 2019: 6:00-9:00pm
Lecture Thursday 14 Feb 2019: 6:00-9:00pm
Lecture Thursday 21 Feb 2019: 6:00-9:00pm
Assessment Thursday 28 Feb 2019: 6:00-8:00pm (2hr)

Lectured By 
Prof Lawrence Wong
This module will cover wireless networks that are relevant to Internet of Things (IoT). The module provides the concepts and operational details of multi-hop, mesh, ad hoc and personal are networks. It also covers aspects such as medium access control, routing and transport protocols.
EE5024 Sensor Networks
Class Schedule 
AY18/19
Semester 2

Lecture Sessions
Lecture Thursday 7 March 2019: 6:00-9:00pm
Lecture Thursday 14 March 2019: 6:00-9:00pm
Lecture Thursday 21 March 2019: 6:00-9:00pm
Lecture Thursday 28 March 2019: 6:00-9:00pm
Lecture Thursday 4 April 2019: 6:00-9:00pm
Lecture Thursday 11 April 2019: 6:00-9:00pm
Assignment Assessment Thursday 18 April 2019: 6:00-9:00pm 
(include 30 mins presentation slot)
Written Assessment Thursday 25 April 2019: 6:00-7:30pm (1.5hr)

Lectured By 
Assoc. Prof Tham Chen Khong
Sensor networks feature prominently in the Internet of things (IoT). This module covers the principles of wireless sensor networks that enables visibility into the physical processes happening around us. Pertinent issues such as energy management and distributed information processing leading to applications such as event detection will be covered. The coupled relationship between wireless sensor network performance, information processing, e.g. at edge computing nodes, and networking protocols, together with energy considerations will be emphasized.
EE5025 Intellectual Property: Innovations in IoT
Class Schedule 
AY19/20
Semester 1

Lecture Sessions
Lecture Mondays (week: 7-13 tentative) 6pm to 9pm

Lectured By 
Assoc. Prof Hari HK Garg
This module takes a hands-on approach to IP management from early stages of technology and inventions to the later stages of commercialization for IoT related technologies. The idea is to provide pragmatic knowledge dealing with one of the most exciting avenues for economic growth and wealth creation. Those planning to pursue the path of a practicing engineer will find the module most useful.
EE5026 Machine Learning for Data Analytics
Class Schedule 
AY18/19
Semester 1

Lecture Sessions
Lecture Thursday 4 Oct 2018 6pm-9pm 
Lecture Thursday 11 Oct 2018 6pm-9pm 
Lecture Thursday 18 Oct 2018 6pm-9pm 
Lecture Thursday 25 Oct 2018 6pm-9pm 
Lecture Thursday 1 Nov 2018 6pm-9pm 
Lecture Thursday 8 Nov 2018 6pm-9pm 
Lecture Thursday 15 Nov 2018 6pm-9pm (Guest Lecture from Industry) 
Assessment Thursday 22 Nov 2018 6pm-7.30pm 
 
Class Schedule
AY18/19
Semester 2

Lecture Sessions
Lecture Wednesday 6 March 2019: 6:00-9:00pm
Lecture Wednesday 13 March 2019: 6:00-9:00pm
Lecture Wednesday 20 March 2019: 6:00-9:00pm
Lecture Wednesday 27 March 2019: 6:00-9:00pm
Lecture Wednesday 3 April 2019: 6:00-9:00pm
Lecture Wednesday 10 April 2019: 6:00-9:00pm
Lecture Wednesday 17 April 2019: 6:00-9:00pm
Assessment Wednesday 24 April 2019: 6:00-8:00pm (2hr)

Lectured by
Asst. Prof Feng Jiashi
This course introduces machine learning methods and their applications for data analytics. Students taking this course will learn modern machine learning techniques including classification, regression and generative models and algorithms as well as how to apply them to data analytics. The course starts with machine learning basics and some classical machine learning methods, followed by supervised and unsupervised data clustering, data dimensional reduction for visualization and data classification. The students are expected to have solid background knowledge on calculus, linear algebra, probability and basic statistics.
EE5027 Statistical Pattern Recognition
Class Schedule 
AY18/19
Semester 1

Lecture Sessions
Lecture Thu 16 Aug 2018 6pm-9pm 
Lecture Thu 23 Aug 2018 6pm-9pm 
Lecture Thu 30 Aug 2018 6pm-9pm 
Lecture Thu 6 Sep 2018 6pm-9pm 
Lecture Thu 13 Sep 2018 6pm-9pm 
Lecture Thu 20 Sep 2018 6pm-9pm 
Assessment Sat 6 October 9am-10.30am 

Class Schedule 
AY18/19
Semester 2

Lecture Sessions
Lecture Wednesday 16 Jan 2019: 6:00-9:00pm
Lecture Wednesday 23 Jan 2019: 6:00-9:00pm
Lecture Wednesday 30 Jan 2019: 6:00-9:00pm
Lecture Saturday 9th Feb 2019: 9:00am-12:00pm
Lecture Wednesday 13 Feb 2019: 6:00-9:00pm
Lecture Wednesday 20 Feb 2019: 6:00-9:00pm
Assessment Saturday 9 Mar 2019: 9:00am-10:30am (1.5hr)


Lectured By 
Asst. Prof Thomas Yeo 
The main objectives of this graduate module are to equip students with the fundamentals of statistical pattern recognition (SPR) algorithms and techniques. PR deals with automated classification, identification, and/or characterization of signals/data from various sources. Because real world data is noisy and uncertain, we will focus on SPR techniques, with particular emphasis on the theoretical foundations of various techniques. Topics covered include: fundamentals of parameter estimation (maximum likelihood, maximum-a-posteriori, posterior predictive), supervised learning, generative models, naive Bayes, discriminative models, logistic regression, non-parametric techniques, Bayesian decision theory.
EE5060 Sensors and Instrumentation for Automation
Class Schedule 
AY18/19
Semester 2

Lecture Sessions
Lecture Wednesday 16 Jan 2019: 6:00-9:00pm
Lecture Wednesday 23 Jan 2019: 6:00-9:00pm
Lecture Wednesday 30 Jan 2019: 6:00-9:00pm
Lecture Wednesday 13 Feb 2019: 6:00-9:00pm
Lecture Wednesday 20 Feb 2019: 6:00-9:00pm
Lecture Wednesday 27 Feb 2019: 6:00-9:00pm
Test, continual assessment. 100% CA type.

Lectured By 
Prof Tan Kok Kiong and Prof Sam Ge SZ
The module offers students timely and updated coverage of a wide range of topics relevant to smart sensor and instrumentation tapping on the latest and diverse range of developments in the repertoire of the control group and collaborating companies and institutions, such as the delivery of a measured collation of case studies of smart sensor and instrumentation applied to real problems of a diverse nature and which are not easily and directly available from standard literature. The nature of the module allows the flexibility for recent topics, problems and solutions to be shared with the students. Students will learn to appreciate the working principles and benefits offered by different types of smart sensors and instrumentation and their applications, and relate smart sensor and instrumentation to IIoT and general digital transformation.
EE5061 Industrial Control and IEC Programming
Class Schedule 
AY18/19
Semester 2

Lecture Sessions
Lecture Wednesday 6 March 2019: 6:00-9:00pm
Lecture Wednesday 13 March 2019: 6:00-9:00pm
Lecture Wednesday 20 March 2019: 6:00-9:00pm
Lecture Wednesday 27 March 2019: 6:00-9:00pm
Lecture Wednesday 3 April 2019: 6:00-9:00pm
Lecture Wednesday 10 April 2019: 6:00-9:00pm
Lecture Wednesday 17 April 2019: 6:00-9.00pm
Test, continual assessment. 100% CA type.

Lectured By 
Prof Tan Kok Kiong and Prof Sam Ge SZ
The module offers students timely and updated coverage of a wide range of topics relevant to common industrial practice and control, tapping on the latest and diverse range of developments in the repertoire of the control group and collaborating companies and institution, such as the delivery of a measured collation of case studies of industrial control applied to real problems of a diverse nature and which are not easily and directly available from standard literature. The nature of the module allows the flexibility for recent topics, problems and solutions to be shared with the students. Students will learn to appreciate the working principles and implementation of industrial controllers commonly found in industry and inter-operability with control programming languages.


Application for Graduate Certificate  

For students to be considered to have satisfactorily completed this programme and be eligible for the awarding of the qualification Graduate Certificate, they must have completed all scheduled course work requirements and assessments, successfully completed assessment requirements for all papers, and achieved a total credits as specified in the programme schedule. The timeframe for completion of programme is three years. Students can do individual PGMC modules click here without registering for GC. The credits earned from these modules can be considered for the GC once registered.



Enquiries

For individual enquiries or corporate classes, please contact:

Leena Nakulan (Ms)
Phone 6516 2167
Email eleln@nus.edu.sg