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Short Course

Explainable Artificial Intelligence Fundamentals

Programme ScheduleDuration (days)Mode of DeliveryProgramme Code
To be advised2Synchronous e-LearningTGS-2022012347

Course Overview

Algorithms in Artificial Intelligence (AI) can be categorised into two types: white-box and black-box algorithms. White-box models generate results that are easily comprehended by domain experts. Conversely, black-box models are challenging to explain and comprehend, even by domain experts. EXAI algorithms embody the principles of transparency, interpretability, and explainability. The role of AI in our lives and businesses will become increasingly important, making it essential that AI systems have outcomes and decisions that are explainable and understandable.

Course Objectives

After completing this short course, the participants will be able to:

  • Articulate EXAI and its applications
  • Realize the importance and applications of EXAI
  • Define different types of explainability
  • Recognize when explainability is necessary

Who Should Attend

AI practitioners, ICT engineers and business managers.

Prerequisites

Basic AI and Machine Learning knowledge

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This programme is part of

  • Professional Certificate in Artificial Intelligence for Business

    • Types of Available Fundings

  • SkillsFuture Funding
  • SkillsFuture Credit
  • Enhanced Training Support
  • SFEC
  • UTAP
  • PSEA

    • Instructors


    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.

    Tomithy Too

    Tomithy Too

    Tomithy invests with ST Telemedia – a global VC/PE fund which invests in Telcos, Data Centres, growth stage tech companies in the Artificial Intelligence, Cybersecurity and Cloud IT. As the technology investor, Tomithy actively works with target and existing portfolio companies on technical diligence, business strategy and commercial evaluation via their CTO, product & engineering teams. He is knowledgeable about enterprise software technology, trends and market dynamics in the US, China and Israel.

    He has a deep interest in governance and reliability of AI systems, generative AI, multi-agent AI systems, evolutionary algorithms and synthetic biology. Tomithy holds a degree in Computational Biology (Hons) from NUS, Masters in AI (SMU) and is currently pursuing a part-time PhD in AI Governance at SMU.

    Programme Fees


    Singapore Citizens Singapore PRs Enhanced Training Support for SMEs International Participants
    39 years old
    or younger    		 40 years old
    or older
    Full Programme Fee S$1,900.00 S$1,900.00 S$1,900.00 S$1,900.00 S$1,900.00
    Less: SSG Grant Amount S$1,330.00 S$1,330.00 S$1,330.00 S$1,330.00 -
    Nett Programme Fee S$570.00 S$570.00 S$570.00 S$570.00 S$1,900.00
    9% GST on Nett Programme Fee S$51.30 S$51.30 S$51.30 S$51.30 S$171.00
    Total Nett Programme Fee Payable, Including GST S$621.30 S$621.30 S$621.30 S$621.30 S$2,071.00
    Less Additional Funding - S$380.00 - S$380.00 -
    Total Nett Programme Fee Payable, Including GST, after additional funding from the various funding schemes S$621.30 S$241.30 S$621.30 S$241.30 S$2,071.00
    1. Total Nett Programme Fee Payable, including GST, after additional funding from various funding schemes.
    2. Participants must fulfill at least 75% attendance and pass all assessment components to be eligible for SSG funding.
    3. Please note that all external funding for courses is limited in duration and subject to eligibility and availability.

    Other Information

    This course is eligible for Union Training Assistance Programme (UTAP). NTUC members can enjoy up to 50% funding (capped at $250 per year) under UTAP. NTUC members aged 40 and above can enjoy higher funding support up to $500 per individual each year, capped at 50% of unfunded course fees, for courses attended between 1 July 2020 to 31 December 2025. Please click here for more information.

    Advanced Computing for Executives

    11 Research Link
    COM 3, #B1-07
    Singapore 119391
    Email: soc-ace@nus.edu.sg

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