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Advanced Professional Certificate

Advanced Professional Certificate in Applied Artificial Intelligence and Machine Learning

Training Roadmap


Completion of Stages 1 to 3 curricula earns the participant an Advanced Professional Certificate in this programme.

Programme Overview

The Advanced Professional Certificate in Applied Artificial Intelligence and Machine Learning offers a comprehensive curriculum designed to equip learners with a solid foundation in AI and ML. Participants will gain a clear understanding of fundamental AI/ML concepts, applications, and the mechanics of various popular algorithms in that area. The course covers the use of RapidMiner for implementing ML modules, alongside Python for developing simple ML applications. It also introduces the basics of Natural Language Processing (NLP) and progresses to advanced deep learning techniques, highlighting their strengths, limitations, and business applications. Learners will acquire practical skills to employ deep learning algorithms for creating simple NLP systems, ensuring a robust and hands-on learning experience.

Programme Objectives

This Advanced Professional Certificate will equip learners with the following competencies

  • Articulating basic definitions and applications of AI and Machine Learning.
  • Understanding the AI/ML approaches and constraints, and how AI/ML algorithms work.
  • Learning how to use Rapidminer to implement Machine Learning modules.
  • Learning Python for the development of simple ML modules.
  • Articulating Natural Language Processing basics.
  • Acquire basic to advanced deep learning knowledge and understand their strengths and constraints, and their applications in different businesses.
  • Have basic skills in using deep learning algorithms to implement simple Natural Language Processing (NLP) systems.

Job Role Readiness

It will prepare learners in the following job roles to perform their responsibilities more effectively:

  • AI and Machine Learning executives / Engineers
  • Business/Industry transformers – Business advisors/analysts
  • ICT engineers, technicians, and executives
  • Data analysts / Business Analysts

Who Should Attend

  • All businesses and industries that would like to employ AI and Machine Learning professionals.
  • All businesses and industries that are going to transform their business model into an AI-driven one.
  • IT Professionals who plan to be AI/ML Engineers and Data Analysts.
  • Novice and mid-career job seekers who would like to join AI/ML teams.

Prerequisites

At least a polytechnic diploma (or equivalent)

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Instructors


A/Prof Danny Poo

A/Prof Danny Poo

A/Prof Danny Poo brings with him 35 years of Software Engineering and Information Technology and Management experience. A graduate from the University of Manchester Institute of Science and Technology (UMIST), England, Dr Poo is currently an Associate Professor at the Department of Information Systems and Analytics, National University of Singapore. Prior to joining the University, Dr Poo was with the System Operations at DBSBank, Singapore. A Steering Committee member of the Asia-Pacific Software Engineering Conference, Dr Poo is actively involved in Information Management and Healthcare Analytics research. A well-known speaker in seminars, Dr Poo has conducted numerous in-house training and consultancy for organizations, both locally and regionally. Dr Poo is the author of five books on Object-Oriented Software Engineering, Java Programming language and Enterprise JavaBeans.

Dr Poo notable teaching credentials include:

  • Data Strategy
  • Data StoryTelling
  • Data Visualisation
  • Data Analytics
  • Machine Learning
  • Data Management
  • Data Governance
  • Data Architecture
  • Capstone Projects for Business Analytics
  • Software Engineering
  • Server-side Systems Design and Development
  • Information Technology Project Management
  • Health Informatics
  • Healthcare Analytics
  • Health Informatics Leadership.
Industry Credentials
  • Deutsche Bank
  • Gemplus
  • Micron
  • NCR
  • PIL
  • PSA
  • Rhode-Schwarz
  • Standard Chartered Bank
  • ST Electronic
  • Monetary Authority of Singapore
  • Infocomm Development Authority
  • National Library Board
  • Ministry of Manpower
  • Nanyang Technological University
  • Nanyang Polytechnic
  • National University Hospital.

Dr Ai Xin

Dr Ai Xin

Dr Ai Xin is currently a Lecturer with the School of Computing at the National University of Singapore (NUS). She has many years’ experience teaching Artificial Intelligence and Data Science courses such as machine learning, deep learning and data mining. She graduated from NUS with a PhD degree in Electrical and Computer Engineering. Her research focused on Game Theoretical Modelling, Optimization Methods, Algorithm Design and Wireless Networks. She worked at BHP Billiton Marketing Asia for eight years, gaining extensive industry experience in various functions such as risk management, supply chain management, and sales and marketing planning.

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.

Dr Lek Hsiang Hui

Dr Lek Hsiang Hui

Dr Lek Hsiang Hui is a Specialist in Data Analytics, System Analysis and Development. He is an IT techie who is passionate about computer systems and technology. He is involved in a few startups and is constantly looking into innovative IT solutions which can be translated to business ideas, with the recent one in the area of Big Data Analytics. He is also a mobile application developer and has produced more than 10 mobile apps. Dr Lek has been teaching various undergraduate courses and executive courses in National University of Singapore (NUS) since 2006. Some of these modules include programming methodology, enterprise system analysis and development, and data mining. During this period, he has won a number of teaching awards such as NUS Annual Teaching Excellence Award (2015/16, 2016/17, 2017/18) and NUS Annual Teaching Excellence Honor Roll (2018/19), Faculty Teaching Excellence Award (2014/15, 2015/16, 2016/17), and Faculty Teaching Excellence Award Honor Roll (2017/18). Dr Lek received his Doctor of Philosophy (Information Systems) from NUS in 2013. His research area is in Natural Language Processing (Sentiment Analysis). He graduated with a Bachelor’s Degree (1st class Honors) in Computer Engineering from NUS. Industry Credentials Shopping Malls (UOL, Marina Square, ION), Healthcare (SingHealth), Government (STB), various SMEs in F&B, Apparels, Cleaning Solutions, Photography, Health Products, Baby Products

Dr Sanka Rasnayaka

Dr Sanka Rasnayaka

I am a lecturer at the School of Computing, National University of Singapore.

My teaching philosophy is to facilitate self-learning by guiding students' thinking towards the final learning outcome, rather than explaining to them how to get there. I enjoy teaching fundamental courses in Computer Science like Discrete Structures, Programming, Algorithms and specialised courses in AI, Machine Learning and Computer Vision.

My research interests are on Continuous Authentication for modern personal devices, on-body gait biometric, privacy and human perceptions.

A/Prof Tan Wee Kek

A/Prof Tan Wee Kek

A/Prof Tan Wee Kek is currently an Associate Professor in the Department of Information Systems and Analytics at the School of Computing, National University of Singapore. He is also currently serving as an Assistant Dean (Student Life) in the School of Computing, and a Fellow and EXCO member of the NUS Teaching Academy. He graduated with a Doctor of Philosophy in Information Systems in July 2013 and a Bachelor of Computing in Information Systems (1st Class Honours) in July 2007, both from the National University of Singapore. Prior to this, he attended Singapore Polytechnic and graduated with a Diploma in Computer Information Systems with Merit in July 2001. His current primary research interests focus on consumer-based information technology (e.g., online decision aids, social computing, virtual worlds and consumer cloud services). Most of his research is based on design science, a well-established problem-solving paradigm that has been widely adopted in information systems research. His current secondary research interests focus on information systems education. His work has been published or is forthcoming in journals such as Journal of the American Society for Information Science and Technology (JASIST), Decision Support Systems (DSS), Communications of the Association for Information Systems (CAIS), and Journal of Information Systems Education (JISE). His work has also been presented or is forthcoming in conferences such as ACM SIGMIS Computer Personnel Research Conference (SIGMIS-CPR), IFIP Working Group 8.2 Working Conference (IFIP WG8.2), European Conference on Information Systems (ECIS), Americas Conference on Information Systems (AMCIS), and International Conference on Human-Computer Interaction (ICHCI). He has won the faculty level Research Achievement Award in 2013 and was a nominee for the ACM SIGMIS 2009 Magid Igbaria Outstanding Conference Paper of the Year. His current teaching interests include imparting senior undergraduate students with knowledge and skills to develop enterprise information systems as well as teaching them principles of information security management, and concepts of mobile and ubiquitous commerce. He has won the University level Annual Teaching Excellence Award for AY 2009 to 2012. In addition to this, he has also won the Faculty Teaching Excellence Award for AY 2008 to 2011 as well as the Faculty Best Teaching Assistant Award for AY 2007/2008. For his sustained commitment to teaching excellence, he has been placed on the Faculty Teaching Excellence Award Honour Roll for AY 2010/2011 and University level Annual Teaching Excellence Award Honour Roll for AY 2012/2013. He is presently the lead faculty mentor of the NUS BiZiT Society, a student special interest group on business and information technology.

Dr Leong Wai Kay

Dr Leong Wai Kay

Dr Leong Wai Kay is a lecturer with the Department of Computer Science at National University of Singapore (NUS). He graduated with Bachelor of Computing and PhD in Computer Science from National University of Singapore. He is currently teaching undergraduate courses in programming methodology, and software engineering on iOS platform.

Dr Yeo Wee Kiang

Dr Yeo Wee Kiang

Dr Yeo Wee Kiang is a Senior Lecturer at the Department of Information Systems and Analytics in the NUS School of Computing, where he is dedicated to researching the use of technologies like Adaptive Learning and Large Language Models (LLMs) to create personalized and engaging computing education experiences. With a PhD from NUS focused on machine learning and data mining for pharmaceutical drug discovery, Dr Yeo brings practical industry experience as the former Director of Data Science at a food tech startup, where he led a team applying AI and machine learning to enhance nutritional value. His diverse background includes being an Adjunct Lecturer in the Department of Economics at NUS Faculty of Arts and Social Sciences, helming the Big Data Analytics module in the Master of Economics programme from 2019 to 2023, and teaching Python programming and data analytics at Nanyang Business School. Prior to his academic appointments, Dr Yeo was the Lead Instructor for Data Science at a New York-based commercial Continuing Education and Training (CET) provider, teaching aspiring adult learners in Hong Kong, Sydney, and Singapore. He also holds the WSQ Advanced Certificate in Training and Assessment (ACTA) from the Institute for Adult Learning Singapore. As part of the NUS ACE (Advanced Computing for Executives) teaching team, Dr Yeo specializes in technical training and practical computing education focused on data analytics, machine learning, and Python, aiming to equip professionals with in-demand Data Science skills.

Dr Edmund Low

Dr Edmund Low

Dr Edmund Low is currently Senior Lecturer with the NUS College at the National University of Singapore. He has nearly 20 years of academic and professional experience in the use of data-driven tools to answer questions in public health and the environment. His past projects include applying AI techniques and machine learning models for environmental modelling and impact assessment. He currently heads the quantitative reasoning domain at USP, and teaches courses on statistical methods, data science and machine learning. As an educator, Edmund is a multiple recipient of both the USP Teaching Excellence Award, as well as the NUS Annual Teaching Excellence Award. Edmund holds a PhD in Environmental Engineering from Yale University.

A/Prof Xavier Bresson

A/Prof Xavier Bresson

A/Prof Xavier Bresson is an international leader in the field of deep learning. Particularly, he co-pioneered a new machine learning technology called graph neural networks (GNN), which combines graph theory and neural network techniques to tackle complex data domains. He has organised several international conferences and tutorials on graph deep learning such as the recent UCLA’21 workshop on “Deep Learning and Combinatorial Optimization”, the MLSys’21 workshop on “Graph Neural Networks and Systems”, the UCLA’19 workshop on “New Deep Learning Techniques”, and the NeurIPS’17, CVPR’17 and SIAM’18 tutorials on “Geometric Deep Learning on Graphs and Manifolds”. He has been a speaker at the top machine learning conferences KDD’21, AAAI’21, ICLR’20 and ICML’20. In 2002, he co-developed with Turing award winner Yoshua Bengio a new class of expressive GNN. He has published more than 70 peer-reviewed articles, which have been cited 13,500+. A/Prof Bresson has been the main lecturer of undergraduate deep learning courses since 2015 at EPFL, NTU and NUS, and has received outstanding evaluations for his teaching material and engaging style. He has also taught graduate courses on advanced deep learning at NUS and was a guest lecturer for Turing award winner Yann LeCun’s course at NYU. He has provided industrial short courses on AI and Deep Learning to Fortune 500 companies s.a. Deloitte, UnitedHealth and university alumni training centres at UCLA, EPFL, and NTU.

Programme Fees


Singapore Citizens Singapore PRs Enhanced Training Support for SMEs International Participants
39 years old or younger 40 years old or older
S$6,147.60 S$2,387.60 S$6,147.60 S$2,387.60 S$20,492.00
  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 for all short courses within an advanced professional certificate 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.

Advanced Computing for Executives

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

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