FlexiMasters in Artificial Intelligence Fundamentals

 Principles of Artificial Intelligence and Ethics: Applications and Responsible Practices
(3 AU)

This course provides a comprehensive coverage in Artificial Intelligence (AI) with a strong emphasis on practical and responsible applications. Learners will deepen their understanding of core AI concepts, computational problem solving approaches, and advance into agent based systems, expert systems, and machine learning techniques. Through practical examples and structured analytical exercises that mirror graduate level expectations, learners will be equipped to solve real world problems responsibly whilst addressing industry needs for ethical AI deployment. This course works towards enhancing learners’ competencies, improving employability, and allowing learners to stay ahead in the evolving Information Technology sector for career progression or job upgrading in technology driven environments.

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

• Describe the key features of Artificial Intelligence (AI) and analyse its strategic relevance to Information Technology (IT) and Society.
  
• Identify and evaluate the ethics, governance, and regulatory considerations involved in deploying AI solutions in enterprise environments.
  
• Utilise advanced ethical Al applications and improve the societal impact of AI systems.
  
• Describe and compare advanced Al applications and topics, including agentic AI, applied expert systems, machine learning and determine their suitability for solving IT industry specific problems.
  

Machine Learning: Methodologies and Applications 
(3 AU)

This course is designed to provide a comprehensive overview of core machine learning techniques, including supervised, unsupervised, and reinforcement learning. The curriculum blends theoretical grounding with hands-on learning, enabling learners to structure machine learning problems, apply and compare models, and interpret results using industry standard Python libraries such as SciKit Learn and SciPy. Emphasising the integration of theoretical constructs with applied problem solving, learners will be encouraged to justify model choices, analyse trade‑offs, and extract robust insights in real world contexts. With the growing demand for practical machine learning expertise in the technology and data science sectors, this course is highly relevant for those seeking to upskill or transit into these industries.

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.

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.

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.