Expert Systems
Dates: To be advised
Duration: 2 Days
Course Overview
Expert systems (ES) attempt to solve complex problems by reasoning through bodies of knowledge, represented mainly as if–then rules rather than through conventional procedural code. A decision support system (DSS) is an information system that supports business or organisational decision-making activities. DSSs serve the management, operations and planning levels of an organization, consequently help people make decisions about complex cases, for example in dealing with unstructured and semi-structured decision problems. Decision support systems can be either fully computerized or human-powered, or a combination of both. In many use cases, DSSs can help businesses. From health management systems, to finance sector, and urban planning, expert systems and DSSs provide diagnostic, decision making, and reasoning services. In this course, we are going to address fundamentals, theory, and practice of expert systems and DSSs.
This course is part of Professional Certificate in Artificial Intelligence for Business.
Course Objectives
The course is devoted to introduce expert systems and decision support systems; show their relationship to different applications in different businesses, and how to design and develop them. Learners will be able to articulate ES and DSS design, implementation approaches and tools.
Who Should Attend
AI experts, AI advisors and AI managers
Prerequisites
- Basic AI and ML knowledge
Course Conveners
(Click their photos to view their short biographies)
Dr Akshay Narayan

Akshay Narayan
Akshay Narayan is a lecturer at the School of Computing in NUS. He received his Ph.D. degree from NUS in 2020 and M.Tech from IIIT-Bangalore in 2012. Akshay teaches senior undergraduate/graduate level AI Planning and Decision Making and introductory Software Engineering modules at SoC.
Akshay is broadly interested in AI planning, sequential decision making, and its applications. His current research is on transfer learning in reinforcement learning. Previously he has worked in the area of cloud computing. Some of his past projects include smart metering and chargeback as applied to cloud systems, incorporating power awareness in cloud metering, workload analysis for virtual machine sizing and location in private clouds, and QoS monitoring and response in cloud systems.
Dr Amirhassan Monajemi

Dr Amirhassan Monajemi
Dr Amirhassan Monajemi is a Senior Lecturer in AI and Machine Learning with the School of Computing (SoC) at the National University of Singapore (NUS). Prior to SoC, he was a Senior Lecturer in NUS School of Continuing and Lifelong Education (SCALE) teaching AI and Data Science to adult learners. Before joining NUS, he was with the Faculty of Computer Engineering, University of Isfahan, Iran, where he was serving as a professor of AI, Machine Learning, and Data Science. He was born in Isfahan, Iran. He studied towards BSc and MSc in Computer Engineering at Isfahan University of Technology (IUT), and Shiraz University respectively. He got his PhD in computer engineering, pattern recognition and image processing, from the University of Bristol, Bristol, England, in 2005. His research interests include AI, Machine Learning, Machine Vision, IoT, Data Science, and their applications.
He has taught the artificial intelligence courses, including AI, Advanced AI, Expert Systems, Decision Support Systems, Neural Networks, and Cognitive Science since 2005 at both undergraduate and postgraduate levels. He was awarded the best university teacher of the province in 2012. He also has studied Learning Management Systems, E-Learning, and E-Learning for workplaces since 2007.
Dr Monajemi has registered a few patents in the fields of AI, Machine Vision, and Signal Processing applications, including an AI and machine vision-based driver drowsiness detection system and a low power consuming spherical robot. He also has published more than a hundred research papers in peer-reviewed, indexed journals and international conferences (IEEE, Elsevier, Springer, and so on), and supervised several Data Science, IoT, and AI industrial projects in various scales, including Isfahan intelligent traffic system delivery and testing, and red light runners detection. He is experienced in different sub-domains of Artificial Intelligence and Machine Learning, from theory to practice, including Deep Learning, Logic, and Optimisation.
Course Fees
Total Nett Programme Fee Payable, Including GST, after additional funding from the various funding schemes
Participants must fulfill at least 75% attendance and pass all assessment components to be eligible for SSG funding.
To enquire, email soc-ace@nus.edu.sg
To register, click Register
Course Codes
TGS-2022012351 (Classroom Learning)
TGS-2022012352 (Synchronous e-learning)
Course Fee Breakdown
Singapore Citizens
39 years old or youngerSingapore Citizen
40 years old or older