FlexiMasters in Artificial Intelligence Fundamentals

 Introduction to Artificial Intelligence (AI) and AI Ethics
(3 AU)

This course introduces the basics of Artificial Intelligence (AI) and on ethical considerations and societal impact. It offers an interdisciplinary overview of AI and Learners will explore key AI concepts, paradigms, and computational problem-solving techniques, as well as advanced topics such as agentic AI, applied expert systems, and machine learning. Learning integrating theory with practical example, learners will be equipped with foundational skills to structure problems, assess AI solutions and apply AI techniques responsibly in real word contexts by the end of the course. 

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

• Describe the key features of Artificial Intelligence (AI) and discuss its importance for Information Technology (IT) and Society. 
  
• Identify and evaluate the ethics involved in the application of Al techniques. 
  
• Utilise advanced ethical Al applications on AI systems for social good.
  
• Describe and compare advanced Al applications and topics, including agentic AI, applied expert systems, and machine learning. 
  

Machine Learning: Methodologies and Applications 
(3 AU)

This course provides a practical introduction to machine learning, covering key concepts in supervised, unsupervised, and reinforcement learning. Learners will understand the notations and formulations of machine learning problems and gain hands-on experience applying loss functions using widely used Python libraries such as SciKit Learn and SciPy. The course emphasises both theory and application, helping learners evaluate the motivations behind machine learning models and interpret insights from real world scenarios. By the end of the course, learners will be equipped with foundational skills to structure problems, implement solutions, and critically assess machine learning approaches for diverse applications.  

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

Deep Learning and Applications 
(3 AU)

This course equips learners with a deep understanding of modern neural network architectures, including Convolutional Neural Networks (CNNs), Residual Networks, and Transformer Networks, while exploring the design principles behind them. Learners will learn to design and refine advanced deep learning techniques, evaluate performance using rigorous research-based metrics, and apply mathematical analysis to interpret network behaviour. The course emphasises practical skills in hyperparameter tuning and heuristic optimisation for achieving optimal performance. By the end of the course, learners will be prepared to build, analyse, and optimise cutting edge neural architectures for real world applications in AI and machine learning.  

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

• Describe concretely modern neural network architectures including Convolutional Neural Networks (CNNs) and Transformer Networks and discuss design rationales. 
  
• Design and refine existing deep learning techniques and neural architectures and evaluate their performance. 
  
• Apply mathematical analysis and heuristics techniques to interpret neural networks behaviour and systematically tune hyperparameters for optimal performance. 

Mathematics for Artificial Intelligence
(3 AU)

This course provides learners with the essential mathematical foundations for understanding and applying Artificial Intelligence (AI) and Machine Learning techniques. Covering key topics in Linear Algebra and Calculus, learners will learn to solve matrix equations, compute derivatives, and apply vector decompositions, skills that underpin AI model design and optimisation. By the end of the course, learners will be equipped to confidently apply mathematical reasoning to AI problems, support model development and optimisation, and translate these techniques into real world projects and industry relevant AI solutions. 

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

• Apply partial derivatives for AI model training.
  
• Apply liner algebra techniques to solve matrix equations and use linear independence basis to decompose vectors. 
  
• Differentiate matrix equations to compute gradients required for minimising loss functions in deep learning models. 
  
• Determine and apply maximum and minimum values of multiple variable functions. 

Computer Vision 
(3 AU)

This course introduces the core principles of machine learning based computer vision, focusing on image analytics and technologies for detection, recognition, and classification tasks. Learners will explore the mathematical foundations underpinning these techniques and gain practical experience through case studies and real-world projects. The curriculum covers essential algorithms, feature extraction, and model design strategies for building effective vision systems. By the end of the course, learners will be equipped to design and implement machine learning based computer vision solutions to address diverse real-world challenges in areas such as object detection, image recognition, and automated classification. 

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

• Identify various machine learning based computer vision technologies
• Explain how different machine learning technologies are used and implemented in various computer vision problems  
• Describe the basic image analytics concepts and technology including image filtering and features. 
• Analyse state-of-the-art computer vision models (e.g., CNNs, object detection frameworks) and evaluate their strengths, limitations, and applicability to real world tasks.  
• Design and implement computer vision systems for detection, recognition, and classification tasks using modern deep learning techniques.

CLASS SCHEDULE

These courses are part of:

• Graduate Certificate in Artificial Intelligence Fundamentals (6 AU)
• FlexiMasters in Artificial Intelligence Fundamentals (15 AU)
• MSc in Artificial Intelligence (30 AU)

Learners will receive their Statement of Accomplishment (for a grade of D and above) or Certificate of Participation for each course—dependent upon their assessment performance.

Singapore Citizen (SC) and Permanent Resident (PR)

(Up to 70% funding)

Singapore Citizen aged ≥ 40 years old SkillsFuture Mid-career Enhanced Subsidy (MCES)

(Up to 90% funding)

Associate Professor Yu Han
Instructor for: 
Introduction to Artificial Intelligence (AI) and AI Ethics 

Assoc Prof Yu Han is an Associate Professor at the College of Computing and Data Science (CCDS), NTU. Between 2018 and 2024, he served as a Nanyang Assistant Professor (NAP) in CCDS, NTU. He has been a Visiting Scholar at the Department of Computer Science and Engineering, Hong Kong University of Science and Technology (HKUST) from 2017 to 2018. Between 2015 and 2018, he held the prestigious Lee Kuan Yew Post-Doctoral Fellowship (LKY PDF) at the Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly (LILY). Before joining NTU, he worked as an Embedded Software Engineer at Hewlett-Packard (HP) Pte Ltd, Singapore. Assoc Prof Yu specialises in trustworthy federated learning and is experienced in deploying various AI solutions to the industry. His contributions to the field of trustworthy AI and real-world impact have earned him recognition as one of the World's Top 2% Scientists in AI, and selected as one of the JCI Ten Outstanding Young Persons (TOYP) of Singapore.

Professor Bo An is a President's Chair Professor and Head of Division of Artificial Intelligence at the College of Computing and Data Science of the Nanyang Technological University (NTU). He is also Director for Centre of AI-for-X of NTU. He was a Nanyang Assistant Professor during 2014-2018. He received his Ph.D degree in Computer Science from the University of Massachusetts, Amherst. Prof Bo An research interests include artificial intelligence, multi-agent systems, computational game theory, reinforcement learning, automated negotiation, and optimization.He was named to IEEE Intelligent Systems' "AI's 10 to Watch" list for 2018.

Dr. Kwok Sai Hang is adjunct lecturer, former CLASS postdoctoral fellow at Nanyang Technological University. He earned his PhD in Philosophy (2017), Mphil in Philosophy (2013), and BA in Mathematics (2011) from the Hong Kong University of Science and Technology. He is now teaching for the Bachelor of Art in Philosophy and Master of Science in Artificial Intelligence program. His research and teaching focus on phenomenology, intercultural philosophy, and philosophy of science and technology, including logic and AI ethics.

Prof Zhang Hanwang is a faculty at the College of Computing and Data Science (CCDS), NTU. He has received the B.Eng (Hons.) degree in computer science from Zhejiang University, Hangzhou, China, in 2009, and the Ph.D. degree in computer science from the National University of Singapore in 2014. He was a research scientist at the Department of Computer Science, Columbia University, USA and a senior research fellow at the School of Computing, National University of Singapore, Singapore. His research interests include computer vision, multimedia, and social media. 

Assoc Prof Li Boyang Albert is a Nanyang Associate Professor at the College of Computing & Data Science (CCDS), Nanyang Technological University. Before joining CCDS, he held a visiting position at the Alibaba-NTU Singapore Joint Research Institute. Prior to that, he was a Senior Research Scientist at Baidu Research USA from 2018 to 2019, and a Research Scientist and Group Leader at Disney Research Pittsburgh from 2015 to 2017. He received his Ph.D. degree in Computer Science from Georgia Institute of Technology in 2014, and his B.Eng. degree from Nanyang Technological University in 2008. He published more than 45 peer-reviewed papers in top-tier journals and conferences and holds two US patents.

Assoc Prof Kong Wai Kin Adams is an Associate Professor at the College of Computing & Data Science (CCDS), Nanyang Technological University. and the current director of Master of Science in Artificial Intelligence programme.  He received the Ph.D. degree from the University of Waterloo. He is listed in Stanford University's Top 2% Scientists’ Study. His recent research interests include pattern recognition, deep learning and their applications on power systems, healthcare, and biometrics.

Assoc Prof Lu Shijian is an Associate Professor at the College of Computing & Data Science (CCDS), Nanyang Technological University.  He received the Ph.D. degree received his PhD in electrical and computer engineering from the National University of Singapore. Before joining NTU, he took a number of leadership roles in the Institute for Infocomm Research (I2R), under the Agency for Science, Technology, and Research (A*STAR) in Singapore, including Head of Visual Attention Lab, Deputy Head of Satellite Department, Co-Chair of the Image and Pervasive Access Laboratory. His major research interests include image and video analytics, visual intelligence, and machine learning.