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Overview

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Enquiry Please email to iss-blendedlearning@nus.edu.sg

Upcoming Classes

Live Session:
● Live Online Session 1: Fri 27 Jun 2025, 2pm – 4pm*
● Live Online Session 2: Fri 4 Jul 2025, 2pm – 4pm*
● Live Online Session 3: Fri 11 Jul 2025, 2pm – 4pm*

*All sessions are in Singapore Time (GMT+8). 

Live Session:
● Live Online Session 1: Fri 15 Aug 2025, 2pm – 4pm*
● Live Online Session 2: Fri 22 Aug 2025, 2pm – 4pm*
● Live Online Session 3: Fri 29 Aug 2025, 2pm – 4pm*

*All sessions are in Singapore Time (GMT+8). 

Live Session:
● Live Online Session 1: Fri 10 Oct 2025, 2pm – 4pm*
● Live Online Session 2: Fri 17 Oct 2025, 2pm – 4pm*
● Live Online Session 3: Fri 24 Oct 2025, 2pm – 4pm*

*All sessions are in Singapore Time (GMT+8). 

Live Session:
● Live Online Session 1: Fri 27 Mar 2026, 2pm – 4pm*
● Live Online Session 2: Thu 2 Apr 2026, 2pm – 4pm*
● Live Online Session 3: Fri 10 Apr 2026, 2pm – 4pm*

*All sessions are in Singapore Time (GMT+8). 



This course is also offered in:
  • In-person Learning (in Singapore): Learn more and register here
  • Live Online Learning (via Zoom): Available for corporate groups only. Contact us at (65) 6516 2093 or email us at iss-blendedlearning@nus.edu.sg

As Singapore increases our focus to excel in the fast-changing Digital Business Economy, our Smart Nation and Industry 4.0 initiatives are aimed at developing people-centric solutions to address global urban challenges by harnessing info-communications technologies, networks and big data to create solutions enabled by technology. As such, the decision to adopt IPA as one of the key digital transformation initiatives must be considered a top priority by key decision-makers in this new digital era.

Robotic Process Automation (RPA) has successfully demonstrated the ability to improve the overall productivity of between 10% - 20% by automating repetitive, replicable and routine tasks. New research and development incorporating Cognitive Intelligence into RPA; also known as Intelligent Process Automation (IPA), is intended to take RPA further by combining Business Process Reengineering (BPR) with RPA and Machine Learning (ML) to further exploit the opportunities of improving the overall productivity of an organisation and improving customer experience in this Digital Economy. Some studies indicate that the implementation of IPA can potentially extend organisational productivity improvement to beyond 70% - 80% of existing processes.

IPA (augmented with ML) to mimic activities carried out by humans and, over time, learns to do them even better is a fundamental shift from just automating what the human does today. IPA is basically traditional RPA powered with decision-making capabilities from advances in deep learning and cognitive technology. The promise of IPA is to radically enhance efficiency, increased worker performance, reduce operational risks, and improve response times and customer experiences.
 

Key Takeaways

At the end of the course, learners will be able to: 

●  Have practical insights of the key concepts of Robotic Process Automation (RPA) and Intelligent Process Automation (IPA).

●  Establish the right strategy for RPA / IPA implementation through the effective use of the process redesign, routine tasks and workflow automation and decision making via Machine Learning technologies.

●  Analyse the practical use cases for RPA and how it can further be improved via IPA.

●  Select the right RPA / IPA engine, vendors (if appropriate) and implementation strategy for the organisation.

●  Evaluate the challenges and people’s resistance in an RPA / IPA implementation and design a programme to overcome these challenges.

Structure

● Course duration: 3 weeks online. 

●  Learners' time commitment: 3 to 4 hours per week.  

●  This course has 3 live online sessions for group activities, discussions, feedback and interactions. 

● Throughout the course, learners are expected to actively contribute to the group activities and online discussions, and are encouraged to apply the knowledge learnt. 

Who Should Attend

This course is ideal for professionals in Singapore and around the world, as it is conducted entirely online. If you are a digital leader or an ICT professional looking to establish an RPA/IPA strategy, develop RPA/IPA business cases for your organisation, or leverage RPA/IPA to enhance customer experience and improve productivity, we encourage you to register.
 

Instructors

YU Chen Kuang
Mr. YU Chen Kuang

Principal Lecturer & Consultant, Digital Strategy & Leadership Practice

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Fees And Funding

Type of Learners  Course Fee Total course fee payable, including GST
Learners residing in Singapore (SGD) SGD 600 SGD 654
Learners residing outside Singapore (USD) USD 500 USD 500

Things To Note

● Learners are expected to set aside time for group activities before and beyond the course.

● The NUS-ISS Certificate of Completion will be issued to learners who have met the course requirements successfully. This will include attending all synchronous Zoom sessions and completing the learning activities, workshops and assessments (where applicable).

● Read the terms and conditions of NUS-ISS Course Registration here.

● Read about NUS-ISS and Learner’s Commitment and Responsibilities here.

● All classes are subject to confirmation and NUS-ISS will send an acceptance email to learners one week prior to the commencement date.

● All responses to feedback and surveys conducted by NUS-ISS and its partners must be submitted by learners.

● All trainings and assessments will be delivered by NUS-ISS, as described in the course webpage.

● For general enquiries and feedback, please feel free to reach out to us via email at iss-blendedlearning@nus.edu.sg.

Artificial Intelligence

Machine Reasoning (Blended Learning)

This course allows learners to learn comprehensive knowledge of artificial intelligence (AI) fundamentals, automated computer/machine reasoning methods, knowledge discovery & modelling, decision support technologies, and intuitive graphics-based programming skills to design and create intelligent machine reasoning systems to solve real-world problems.

This course provides learners with fundamental machine learning concepts, algorithms and techniques to discover patterns for actionable insights. Learners will learn to use common software tools and libraries found in Machine Learning projects and apply their knowledge to create models and solve challenging problems.

This course equips learners with the knowledge to analyse data more effectively by deriving useful hidden patterns in it. Learners will also learn how to select and apply the most suitable techniques to solve problems and develop pattern recognition systems.