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NUS Institute of Systems Science

NICF- Machine Reasoning (SF)

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This 4-day course enables participants to create intelligent computer software system to make use of knowledge (digitised data useful to business), reason and take actions automatically, in various business contexts and industry domains.

Participants gain 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.

Some examples of systems used in industry are:

  • Business rule management system (BRMS) for credit screening/scoring, Microsoft Outlook email rules & alerts;
  • Business process management system (BPMS) for annual employee performance appraisal, mortgage/loan application & approval;
  • Artificial intelligence virtual player / non-person character (AI NPC) for video game industry; etc.

This course can benefit engineers, scientists, software developer, application solution architect, and information technology professionals who intend to design, develop, implement and evaluate various applications of computer assisted decision automation systems.

Participants will benefit from a careful balance of lectures and practical workshops. Some of the topics covered include concepts and techniques of artificial intelligence fundamentals; machine inference; knowledge representation; system architecture and modelling; knowledge discovery by data mining & machine learning; design and create machine reasoning system as a minimum viable product (MVP), e.g. BRMS/BPMS system. There will be projects and assessment to reinforce participants’ learning as part of the course.

Key Takeaways

Upon completion of the course, participants will be able to:

  • Identify needs of machine reasoning technology in various industrial applications, for decision automation.
  • Acquire knowledge of core machine reasoning techniques, including rule/process-based logical reasoning, domain expert knowledge acquisition and representation, knowledge discovery, and handling uncertainty during reasoning process.
  • Apply data mining / machine learning techniques to extract knowledge from data, then express business rules/processes in computer readable format.
  • Create software application by applying learnt machine reasoning techniques and computer programming.

What Will Be Covered

This course will cover:

Day 1

Machine Reasoning Overview Reasoning Types & System Architectures Machine Reasoning Foundation Workshop

Day 2

  • Knowledge Acquisition & Representation
  • Knowledge Models (from the acquired to the represented)
  • Knowledge Modelling Workshop

Day 3

  • Artificial Intelligence: Technical Machine Inference
  • Knowledge Discovery by Data Mining / Machine Learning
  • Knowledge Discovery Workshop

Day 4

  • Contemporary Reasoning Systems
  • Creating Machine Reasoning System Workshop
  • Course Assessment

Who should attend

This course is suitable for information technology professionals who are interested in creating intelligent computer software system able to make use of knowledge (digitised data useful to business), reason and take actions automatically, in various business contexts and industry domains.

This course will be useful for:

  • Artificial Intelligence Engineer who need develop competency in knowledge modelling, representation, discovery, knowledge graph, knowledge/rule base, and machine inference.
  • Software Developer/Engineer who need develop competency in business rule management system (BRMS) and business process management system (BPMS)
  • Application Solution Architect who need design intelligent system solutions and integrate them into enterprise system architecture
  • Data Scientist/Engineer who need obtain domain knowledge in artificial intelligence to assist data analytics.
  • Working professionals who need to upgrade existing machine reasoning knowledge and skills by practicing contemporary system building tool sets.


GU Zhan (Sam) lectures Master of Technology programme in the areas of data science, machine intelligence, soft computing, and applied deep learning. Prior to joining ISS, he was in New Zealand running Kudos Data start-up, which focussed on training programs to democratize artificial intelligence ...
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