Specialist Diploma in Applied Artificial Intelligence

School of Infocomm, Lifelong learning, Specialist Diploma in Applied Artificial Intelligence

About the Course

Artificial Intelligence (AI) has been identified as one of the frontier technologies that is essential in the growth of Singapore’s Digital Economy.

AI and automation processes are constantly changing the way we work, and workers will have to keep up with workforce transitions and transformations by acquiring new skills. AI has actively contributed to many businesses’ economic growth in terms of productivity, by boosting the capability to complete mundane tasks repetitively and continuously.

The Specialist Diploma in Applied Artificial Intelligence (SDAAI) will equip participants with the necessary concepts and know-how in developing comprehensive AI solutions.

Through the programme, participants will undertake hands-on projects and examine case studies on the application of AI in the areas of computer vision and natural language processing.

Target Audience

Working adults or IT professionals who are seeking acquire knowledge and skills in the AI field, and obtain a formal qualification.

Individuals who using or implementing AI or Machine Learning in their current role.

Recently graduated diploma students looking to raise their skills and knowledge in the area of AI.

Career Opportunities

Job opportunities in the AI field include:

  • Data Scientist
  • AI Engineer
  • Business Intelligence Developer

What is so unique about this programme?

The SDAAI is a 12-month, part-time programme. Lessons have been designed with industry input, and it will provide participants with the relevant hands-on experiences and knowledge that can be applied immediately. The SDAAI will cover two main areas of AI: Computer Vision, and Natural Language Processing.

How often do I need to attend the course?

Lessons will be held at RP, Woodlands Campus.

Course commences in June

Number of Lessons Duration
Three times/week (Weekdays) 6:30pm - 9:30pm

What will I get out of this

Upon graduation, I have expanded my career towards AI project engagement. This has allowed me to contribute my new skillsets to my company, MindCraft.

Example, recently we have sealed a partnership with KeyReply, a key AI platform technologist that is accredited by iHiS, the technology agency for the public healthcare sector in Singapore. Graduating in Specialist Diploma in Applied Artificial Intelligence (SDAAI) has open a new wave of knowledge, participating in upcoming project that improves lives and numerous career opportunities.

I recommend to everyone who embarked in pursuing IT to consider enrolling such courses.
Mr Richard Lim
SDAAI Class of 2021

The Specialist Diploma in Applied Artificial Intelligence consist of 2 Post-Diploma Certificates (PDC). Each certificate consist of 4 or 3 modules each respectively, as shown below:

Post-Diploma Certificate Modules
Fundamentals of Artificial Intelligence
  1. Programming for Artificial Intelligence
  2. Data Management and Ethics
  3. Machine Learning Fundamentals
  4. Deep Learning Fundamentals
Technologies of Artificial Intelligence
  1. Computer Vision
  2. Natural Language Processing
  3. Capstone Project*

• Non Work-Study Programme: Project as RP-based
• Work-Study Programme: Project as Company-based OJT

Module Synopses

1. Post-Diploma Certificate in Fundamentals of Artificial Intelligence

The PDC in Fundamentals of Artificial Intelligence is intended to equip students with the requisite concepts and theories that underpin artificial intelligence and the fundamental practical skills and knowledge for developing AI solutions.

This post-diploma certificate consists of the following 4 modules:

Programming for Artificial Intelligence

This module equips students with the fundamentals of programming using Python. Students will learn how to solve problems through coding a software program. Fundamentals on software structure, variables, selection and iteration constructs will be covered. Students will be able to create software to solve simple programming problems related to AI.

Data Management and Ethics

This module offers an introduction to the use of open-source data platform and framework for storing large bulk of data. It aims to provide an overview of the data life cycle of the machine learning pipeline and running of high-throughput training and evaluation operations of the learning models. The lessons will typically focus on the practices associated with data management and the development of databases for operations, such as real-time data acquisition and preparation. Participants will also examine issues using basic ethical and legal frameworks, reasoning, guidelines and/or principles to create a greater awareness of general ethical and legal issues involved in the field of AI, with a particular focus on privacy and data protection-related issues.

Machine Learning Fundamentals

This module covers basic concepts in machine learning and the use of various open source libraries like scikit-learn, Tensorflow, and Keras to build basic machine learning application with Linear regression, K nearest neighbors, SVMs, decision trees and unsupervised learning. Upon completing the module, participants will be able to explain the essential principles of machine learning, with hands-on experience in building, validating and deploying machine learning models using Python.

Deep Learning Fundamentals

This module aims to introduce participants to the key topics associated with deep learning. This module will cover the fundamental underpinnings of Artificial Neural Networks (ANN), Convolution Neural Networks (CNN), Recurrent Neural Networks (RNN) and Long-Short Term Memory (LSTM) networks and the fundamentals will be supported with practical work that involves participants developing and deploying an ANN, CNN, RNN, and LSTM.

2. Post-Diploma Certificate in Technologies of Artificial Intelligence

This PDC focuses on applying and extending the skills and knowledge of students in 2 specific technologies of artificial intelligence. These AI technologies include Computer Vision and Natural Language Processing.

This post-diploma certificate consists of the following 3 modules:

Computer Vision

The aim of the module is to develop artificial intelligent (AI) solutions for computer vision (CV) problems. The solutions will use classical, modern, or/and cloud-based image processing and analysis methods. This module also covers the analysis and design of the CV problems.
Upon completing the module, students will be able to analyse CV problem, design a solution, develop the solution, enhance the accuracy of the solution and deploy the developed solution.

Natural Language Processing

Natural language processing (NLP) is a branch of artificial intelligence that helps computers to understand, interpret and manipulate human language.
This module aims to introduce students to the development of AI solutions for NLP. Students will acquire knowledge in common NLP tasks and will be able to differentiate the different approaches (traditional, machine learning and deep learning) use to design and perform these tasks. Students will gain hands-on practice using suitable libraries, toolkits and services that allows them to perform NLP related tasks and develop applications that can process, interpret and understand text.

Capstone Project*

In this capstone project, you will demonstrate your competencies in architecting, designing and building AI solutions in response to problem statements. You will apply their AI knowledge and expertise to develop and test their AI model. You will then present a project report to demonstrate the validity of your model and proficiency.

• Non Work-Study Programme: Project as RP-based
• Work-Study Programme: Project as Company-based OJT

Commencement Date Duration Mode and Venue Schedule Application Start and End Date
November 2022 1 Year RP Woodlands Campus
Three times a week (Weekday) 6:30pm - 9:30pm

-Or -

2 Weekday +
Saturday
9am - 12pm   
Jun intake: Jun– Aug 

Application outcome will be released at least 3 weeks prior to course commencement

View Schedule Here


*Application closing date 21 August 2022

  • Applicants should possess a relevant local polytechnic Diploma or Degree in Information Technology or Computer Science from a recognised institution.

  • They must also have at least 5 years of relevant work experience. Applicants who do not possess the above qualification shall be reviewed on a case-by-case basis.

Singapore Citizen

Fee Subsidy Eligibility Post-Diploma Certificate (PDC1) Net Fee Post-Diploma Certificate (PDC2) Net Fee Course Net Fee
SME-sponsored $305.40 $305.40 $610.80
40 years & above $295.32 $295.32 $590.64
39 years & below $449.40 $449.40 $898.80

Singapore Permanent Resident

Fee Subsidy Eligibility Post-Diploma Certificate (PDC1) Net Fee Post-Diploma Certificate (PDC2) Net Fee Course Net Fee
Self-sponsored $1,181.28 $1,181.28 $2,362.56
SME-sponsored $305.40 $305.40 $610.80

Non Singapore Citizen/ Permanent Resident

Post-Diploma Certificate (PDC1) Net Fee Post-Diploma Certificate (PDC2) Net Fee Course Net Fee
$2,966.04 $2,966.04 $5,932.08

Note:
*Payment may be made using SkillsFuture Credit.
*Fees reflected are inclusive of GST (Goods & Service Tax).
*From 1 July 2020, Workfare Training Scheme (WTS) and fee subsidy under WTS will cease. WTS will be replaced by Workfare Skills Support Scheme (WSS). For more information on WSS, please refer to www.wsg.gov.sg/programmes-and-initiatives/workfare-skills-support-scheme-individuals.html

Republic Polytechnic reserves the right to make changes to the course fee and application closing dates without prior notice. The commencement of each course is subject to sufficient number of participants.

All information is accurate at time of publishing.

Last updated on 19 Aug 2022

Need more help?

If you are still unsure about which course to pursue, please contact our Academy for Continuing Education.  

Republic Polytechnic reserves the right to make changes to the course fee and application closing dates without prior notice.  The commencement of each course is subject to a sufficient number of participants. All information is accurate at time of publishing.