FlexiMasters in Signal Processing and Machine Learning

 Real-time DSP Design & Applications
(3 AU)

This course is ideal for engineers and data scientists looking to innovate in Artificial Intelligence (AI), digital communications, and smart system design.

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

• Explain the theoretical principles of signal processing systems, such as finite precision, sampling and quantisation.
• Compare and evaluate different architectures for implementing hardware systems for real-time processing.
• Design basic software solutions using DSP processor for real-time system design.

Natural Language Processing
(3 AU)

• Identify the appropriateness of NLP preprocessing techniques in different contexts.
• Explain the mathematical derivations of traditional NLP methods such as term weighting schemes, feature extraction techniques, and topic modelling.
• Execute simple NLP tasks such as classification and clustering on small-scale problems and evaluate the algorithm performance.
• Implement NLP algorithms in Python programming language.
• Distinguish between the theoretical concepts of traditional and deep neural network-based NLP techniques.
• Formulate the construction of word embeddings and model training using deep neural network-based architecture such as Recurrent Neural Network (RNN), Seq2Seq, Attention mechanism, and transformers.
• Describe the working principles of pre-trained language models.
• Design NLP-based project as a team to solve a real-life application by employing any techniques learnt along with fine-tuning the model.

Analytic & Ensemble Machine Learning
(3 AU)

This course offers a foundational overview of machine learning, examining both analytic and ensemble approaches. Beginning with basic concepts, the course then progresses to linear regression, classification, kernel-based techniques. Learners will then explore ensemble approaches like bagging and boosting, including classic algorithms like Random Forest, AdaBoost, and traditional Gradient Boosting before advancing to the more popular XGBoost, and LightGBM. The curriculum also introduces reinforcement learning and its relationship to ensemble methods, with practical industry examples. Learners can enhance employability and position themselves for roles in Artificial Intelligence (AI) and machine learning engineering upon the completion of this course.

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

• Implement and use data preprocessing, linear models and learning score functions.
• Implement and use analytic regression, classification and kernel methods.
• Implement bagging framework and random forest algorithms to solve classification and regression problems.
• Describe and evaluate the boosting framework, various boosting algorithms, and reinforcement learning.

This course approaches the tackling of complex and real-world problems with genetic algorithms and machine learning. Learners will explore optimisation techniques using evolutionary algorithms for solving challenges with mixed real-integer variables, numerous locally optimal solutions, and discontinuities; and traverse key machine learning concepts, theories and algorithms for analysing supervised and unsupervised learning algorithms. This micro-credential enables learners to leverage machine learning methods for data analytics and pattern recognition. By the end of the course, learners would be able to apply various evolutionary optimization algorithms to solve problems in their own research fields.

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

• Explain the theories of evolutionary algorithms.
• Apply evolutionary algorithms to formulate and solve various optimisation problems
• Analyse supervised learning and unsupervised learning algorithms, and compare various classifiers and clustering algorithms
• Design and implement solutions for real-world problems using appropriate machine learning methods.

Video Analysis and Processing 
(3 AU)

This course is designed to equip learners with comprehensive knowledge in video analysis and processing. It includes principles, concepts and theories, behind image and video compression techniques and standards, Artificial Intelligence (AI) models used for video analytics, key video analytics tasks and applications, as well as recent developments in the field. Learners interested in expanding their expertise in video analytics are encouraged to enroll. Upon completion, learners will possess the theoretical understanding and practical insights necessary to advance their careers in video analysis and processing.

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

• Explain key image compression techniques and standards and assess their impact on image quality and processing efficiency.
• Evaluate various key video compression techniques and standards.
• Analyse the core artificial intelligence models for video analytics.
• Describe some common video analysis tasks and explain their applications.
• Evaluate recent and emerging developments in video analysis and processing, assessing their potential applications, limitations, and implications in real-world deployment.

Real-time DSP Design & Applications

Natural Language Processing

Analytic & Ensemble Machine Learning

Semester 1 and 2

Semester 1 and 2

These courses are part of:

• Graduate Certificate in Signal Processing and Machine Learning (9 AU)
• FlexiMasters in Signal Processing and Machine Learning(15 AU)
• MSc in Signal Processing and Machine Learning (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.

Dr Anamitra Makur
Instructor for: 
Real-time DSP Design & Applications

Anamitra Makur received the B.Tech. degree from the Indian Institute of Technology, Kharagpur, and the M.S. and Ph.D. degrees from the California Institute of Technology, Pasadena. He was with the Indian Institute of Science, Bangalore, until 2002 in various capacities, the most recent being a Professor. He also held visiting positions at University of California at Santa Barbara, University of Kaiserslautern, Griffith University at Brisbane, and Keio University at Yokohama. Since 2002, he has been an Associate Professor at Nanyang Technological University, Singapore.

Dr S Supraja
Instructor for: 
Natural Language Processing

S Supraja received her B.Eng. degree in Electrical and Electronic Engineering (EEE) from Nanyang Technological University (NTU), Singapore, in 2016. Subsequently, Supraja also received her Ph.D degree in EEE from NTU in 2022, under the Delta-NTU Corporate Laboratory. She joined as a EEE Lecturer in 2022, initially focusing on the Interdisciplinary Core Curriculum (ICC) and subsequently other EEE courses. She has championed the use of team-based learning to develop a new postgraduate EEE MSc course EE6405: Natural Language Processing in 2024. At present, she is also the Assistant Chair (Students) as an appointment holder.

Supraja's research interests include natural language processing & machine learning, curriculum design & pedagogical practices, as well as, learning analytics & educational data mining. She has been actively pursuing teaching grants such as the EdeX Teaching and Learning Grant, EdeX Faculty Learning Communities Grant, EEE Teaching Ignition Grant, and NTU-Imperial College London Education Fund. She has also supervised several MSc students under the topics of natural language processing, computer vision, and multimodal systems. She has top-tier journal publications in the area of natural language processing and learning analytics. She was recently awarded with the EEE Early Career Teaching Excellence Award and was a ICC Most Caring Teaching Award Nominee in 2025.

Assoc Prof Lin Zhiping
Instructor for: 
Analytic & Ensemble Machine Learning

Zhiping Lin received Ph.D. degree in information engineering from the University of Cambridge, UK, in 1987. Since 1999, he has been an associate professor at the School of Electrical and Electronic Engineering (EEE), Nanyang Technological University (NTU), Singapore, where he is now serving as Programme Director of MSc in Signal Processing and Machine Learning, the School of EEE, NTU. Prof. Lin served as the Editor-in-Chief of Multidimensional Systems and Signal Processing, and in the editorial board of several other international journals, including the Journal of the Franklin Institute and IEEE Trans. on Circuits and Systems II. He was the coauthor of the 2007 Young Author Best Paper Award from the IEEE Signal Processing Society, and a Distinguished Lecturer of the IEEE Circuits and Systems Society during 2007-2008.

His research interests include multidimensional systems, statistical signal processing and image/video processing, robotics and machine learning. He has published over 200 journal papers and over 200 conference papers. He co-authored the book entitled “Analytic Learning Methods for Pattern Recognition” published by Springer in 2025. He received the 2022 Inspirational Mentor for Koh Boon Hwee Award, and the 2025 EEE Staff Award for Exceptional Contributions in Coordinating and Promoting MSc Programmes from NTU.

Prof Kar-Ann Toh 
Instructor for:
Analytic & Ensemble Machine Learning

Kar-Ann Toh is a Professor in the School of Electrical and Electronic Engineering at Yonsei University, South Korea. He received his Ph.D. degree from Nanyang Technological University (NTU), Singapore, in 1999. After graduation, he worked for two years in the aerospace industry before holding postdoctoral appointments at NTU research centers from 1998 to 2002. He was subsequently affiliated with the Institute for Infocomm Research in Singapore from 2002 to 2005, prior to joining Yonsei University. During his sabbatical year in 2020, he served as a Visiting Professor in the Department of Electrical and Computer Engineering at the National University of Singapore (ECE–NUS).

He is the co-author of the book Analytic Learning Methods for Pattern Recognition (2025). His research interests include pattern classification, machine learning, neural networks, and biometrics. He has served, or is currently serving, as an Associate Editor for several international journals, including IEEE Transactions on Biometrics, Behavior, and Identity Science, IEEE Transactions on Information Forensics and Security, Journal of the Franklin Institute, Pattern Recognition Letters, and IET Biometrics.

Dr. Meng-Hiot Lim is a faculty in the School of Electrical and Electronic Engineering.  He was one of the founding co-directors of the M.Sc. in Financial Engineering and the Centre for Financial Engineering, anchored by the Nanyang Business School. A versatile researcher with diverse interests, his areas of research focus include computational intelligence, machine learning, finance, algorithms for UAVs and memetic computing, and more recently in pedagogical engineering. He was the founding Editor-in-Chief of Memetic Computing Journal by Springer, and currently he serves as the Technical Editor-in-Chief . He is the Chief Editor of PALO (Proceedings on Adaptation, Learning and Optimization) series and is also a Book Series Editor of Smart Systems Technology by Springer. 

He has significant experience in industrial funded projects with companies such as Seiko, ST Engineering, Boeing USA and others. Being a key initiator and founding director of Garage@EEE, he helped to promote and nurture student startups.  He is also a founder and advisor of EEE ViPod club and NTU Uavionics club.

Assoc Prof Mao Kezhi
Instructor for: 
Genetic Algorithms & Machine Learning

Dr. Kezhi Mao obtained his BEng, MEng and PhD from Jinan University, Northeastern University, and University of Sheffield in 1989, 1992 and 1998 respectively. Since 1998, he has been working at School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, where he is an Associate Professor. Dr. Mao's expertise spans several subfields of artificial intelligence (AI), including machine learning, computer vision (CV), natural language processing (NLP), and large language models (LLM).

As the lead Principal Investigator, Dr. Mao has successfully completed over a dozen funded projects that span the vast landscape of AI. As a passionate advocate of translational research, Dr. Mao and his team developed and delivered a range of intelligent systems and tools to government agencies and industries, enriched with AI-driven solutions.

Beyond his research and development endeavours, Dr. Mao is active in conducting consultancy work in the AI landscape. He advised several multinational corporations on AI techniques in the business world, emphasizing the practical applications of the state-of-the-art technologies.

Dr. Kim-Hui Yap received the Bachelor of Electrical Engineering (University Medal Winner and First Class Honors) and PhD degrees from the University of Sydney, Australia. He is currently an Associate Professor at the School of Electrical and Electronic Engineering, Nanyang Technological University. His main research interests include computer vision, artificial intelligence and data analytics. He has authored more than 150 technical publications in various reputable international journals and conference proceedings.