Master of Science in Signal Processing and Machine Learning

Have a good relevant bachelor's degree

Relevant working experience is an advantage

For applicants whose native language is not English, TOEFL/IELTS score is to be submitted with the application for admission:

         TOEFL Score (Test dates must be within 2 years or less from the date of application):

          ≥ 563 (paper-based)

          ≥ 223 (computer-based)

          ≥ 85 (internet-based)

         IELTS Score (Test date must be within 2 years or less from the date of application):

          ≥ 6.0

Applicants without TOEFL/IELTS would still be eligible to apply, but they may be subjected to an interview/test if deemed necessary by the School.

Programme Structure

There are two options of study, one with coursework only, and the other with coursework and dissertation. Each course is of 3 AUs and consists of 39 hours of lectures. Candidates who undertake a project of 6 AUs must submit a dissertation on it. Students are encouraged to choose the dissertation option only if they have a strong interest in pursuing further research studies.

Option 1 Coursework Only	Option 2 Coursework + Dissertation
10 courses (30 AUs in total)	8 courses + Dissertation project (30 AUs in total)
At least 4 specialized electives (≥12 AUs)	At least 4 specialized electives (≥12 AUs)
Not more than 6 general electives (≤ 18 AUs)	Not more than 4 general electives (≤ 12 AUs)
	Dissertation (6 AUs)

 

 Note:  The programme structure will be subject to change without prior notice.

 

Duration

Both full-time and part-time programmes are offered (unless stated). Part-time candidates are expected to obtain permission from their employer before admission to the programme. Most classes are conducted in the evenings, while ex aminations are conducted during office hours.

Type of Coursework Programme	Minimum Candidature	Maximum Candidature
Master of Science (Full-Time)	1 year	3 year
Master of Science (Part-Time)	2 year	4 year

 

Programme Calendar

Semester 1	August to December
Semester 2	January to May
Week 1 to 14	Lecture (Inclusive of 1-week recess)
Week 15 to 17	Examinations
Other	Vacation

Graduate courses offered by Master of Science (MSc) Signal Processing and Machine Learning (from August 2024):

Specialized Elective Courses (Students are required to take a minimum of 4 out of all the 6 specialized elective courses)

Course Code	Course Title	Course Content	AUs
EE6222	MACHINE VISION	Fundamentals of image processing & analysis. Feature Extraction Techniques. Pattern / Object Recognition and Interpretation. Three- Dimensional Computer Vision. Three-Dimensional Recognition Techniques. Biometrics.	3
EE6401	ADVANCED DIGITAL SIGNAL PROCESSING	Discrete-time signal analysis and filter design. Multi-rate digital signal processing. Linear prediction and optimal linear filters. Power spectrum estimation.	3
EE6402	REAL-TIME DSP DESIGN AND APPLICATIONS	Digital Filter Implementation Issues. Advanced Arithmetic Techniques for Hardware. Architecture for General Purpose Digital Signal Processor. Peripherals for DSP Applications. Design and Development Tools for DSP Processors. Introduction to VLSI. Algorithms and Architecture for VLSI.	3
EE6405	NATURAL LANGUAGE PROCESSING	Traditional: Bag-of-words, Preprocessing, Term weighting scheme, Feature extraction,. Topic modeling , ML classifiers and clustering methods, Evaluation Metrics, Word Embeddings Deep Neural Networks: Graph convolutional network, Seq2Seq, Attention mechanism, Transformers and self-attention, Pretrained Language Models, Fine-tuning (hyper-parameter tuning), Applications (chatbot, machine translation, sentiment analysis, summarisation, classification, generation, auto-complete)	3
EE6407	GENETIC ALGORITHMS AND MACHINE LEARNING	Review of Combinatorics and Probability. Introduction of Genetic Algorithms. Differential Evolution. Particle Swarm Optimization. Advanced Techniques. Principles of Machine Learning. Paradigms of Machine Learning. Kernel Methods.	3
EE6427	VIDEO SIGNAL PROCESSING	Image and Video Basics. Image and Video Transform Coding. Filtering and Error Resilience for Image and Video. Image and Video Coding Principles and Standards. Recent and Emerging Topics in Image and Video Processing.	3

 

General Elective Courses

Course Code	Course Title	Course Content	AUs
EE6008	COLLABORATIVE RESEARCH & DEVELOPMENT	Project Charter, Design and prototype development, Project implementation, Testing and instrumentation, Project report, Oral presentation, Demonstration	3
EE6010	PROJECT MANAGEMENT & TECHNOPRENEURSHIP	Project Initiation and Planning. Project Scheduling and Implementation. Project Monitoring, Control and Evaluation. Innovation and Entrepreneurship.	3
EE6101	DIGITAL COMMUNICATION SYSTEMS	Communication signals and baseband transmission. Digital modulation/demodulation. Error correction coding. Spread-spectrum techniques.	3
EE6102	CYBER SECURITY AND BLOCKCHAIN TECHNOLOGY	Cyber Security Threat Landscape, Industry 4.0 and Cyber Security, Cyber Security Education, Awareness and Compliance, Cyber Security Planning, Policies and Compliance, Cyber Security Risk Assessments and Biometric-based Security approaches, Public key Infrastructure (PKI), Web Security and role of firewalls and Intrusion Detection, Online Payment, and Cryptocurrencies. Basics of Blockchain technology, Types of blockchain Technology, Blockchain Technology Applications for Industry 4.0, use cases and real-world case studies	3
EE6129	WIRELESS & MOBILE RADIO SYSTEMS	Wireless channel models. Fading and ISI mitigation techniques. Cellular concept and Multiple access techniques. Satellite communications.	3
EE6204	SYSTEMS ANALYSIS	Linear, Dynamic and Integer Programming. Optimization Techniques. Random Processes. Queuing Models. Markov Decision Process.	3
EE6221	ROBOTICS & INTELLIGENT SENSORS	Overview of robotics. Motion planning and control. Mobile robots . Controller hardware/software systems. Sensorsystems and integration.	3
EE6285	COMPUTATIONAL INTELLIGENCE	Introduction to Fuzzy Logic, Introduction to Fuzzy Sets, Introduction to Fuzzy Inference Systems, Fuzzy Logic Applications, Introduction to Genetic Algorithm, Fundamental Concepts of Artificial Neural Networks and Neural Network Architectures, Neural Network Applications	3
EE6301	SMART BIOSENSORS & SYSTEMS FOR HEALTHCARE	Introduction to biosensors and healthcare; Optical biosensors-fundamentals; Optical biosensors-applications; Biomedical imaging with optical technologies; Introduction to electrical biosensors- fundamentals; Introduction to electrical biosensors- fabrications; Applications of electrical biosensor; Introduction to bio-intelligent systems; Artificial intelligence in medical sensing and imaging	3
EE6341	ADVANCED ANALOG CIRCUITS	Low Noise Circuits, Wide-Bandwidth Amplifiers, Power Amplifiers, Active Filters, DC-DC Converters	3
EE6403	DISTRIBUTED MULTIMEDIA SYSTEMS	Media and Media Systems. Media Compression and Standards. Media Processing and Storage. Media Transmission and Delivery. Quality of Service on Distributed Multimedia Systems. Multimedia Applications.	3
EE6406	Analytic and Ensemble Learning	This course aims to equip students with a foundational understanding of machine learning from both analytic and ensemble perspectives. The curriculum is designed to offer an overview of the key technologies underpinning modern machine learning and deep learning. Initially, the course will recap the basic components of machine learning. It will then delve into fundamental learning techniques, including linear methods for regression, linear methods for classification, and polynomial plus kernel methods—all grounded in linear algebra. Subsequently, students will explore applied ensemble methods based on bagging and boosting. The course will cover classical ensemble methods such as Random Forest, AdaBoost, and standard Gradient Boosting, as well as more advanced boosting techniques, including XGBoost and LightGBM, which find practical applications in the industry. In addition, the course will also introduce reinforcement learning and its connection with ensemble learning. To highlight the practical relevance, the course will include examples from industrial contexts.	3
EE6428	SPECIAL TOPICS IN SIGNAL PROCESSING	Fundamentals of image analysis, image segmentation and evaluation, object representation and description, feature measurements, shape analysis and texture analysis, and mathematic morphology techniques. Applications and case study.	3
EE6483	ARTIFICIAL INTELLIGENCE AND DATA MINING	Structures and Strategies for State Space Representation & Search. Heuristic Search. Data Mining Concepts and Algorithms. Classification and Prediction methods. Unsupervised Learning and Clustering Analysis.	3
EE6497	PATTERN RECOGNITION & DEEP LEARNING	Introduction, probability review, Bayesian Inference, Mixture Models and EM Algorithm, Markov Models and Hidden Markov Models, Sampling, Markov chain Monte Carlo (MCMC), Neural Networks, Deep Learning (CNN, RNN), Training Deep Networks, Deep Network Architectures, Applications, Generative Models, Self-supervised Learning.	3
EE7204	LINEAR SYSTEMS	Input/Output System Models. State Space Representation. Norms of Signals and Systems. Decomposition of Linear Time-Invariant Systems. Linear Feedback Design. Convex Optimization for Linear System Analysis and Design.	3
EE7205	RESEARCH METHODS	Research Preparation and Planning. Research Sources and Review. Quantitative Methods for Data Analysis. Experimental research methods. Academic Writing & Presentation	3
EE7207	NEURAL NETWORKS AND DEEP LEARNING	The key topics to be covered in the context of deep neural networks and deep learning will encompass convolutional neural networks (CNN), modern recurrent neural networks (RNN), the attention mechanism and the transformer, self-supervised learning, graph neural networks, all of which represent the cutting-edge methods in the realm of deep learning. In addition, some typical applications and advanced topics of deep learning will be introduced.	3
EE7401	PROBABILITY AND RANDOM PROCESSES	Probability concepts. Random variables. Multiple random variables. Sum of random variables and multidimensional distributions. Random Sequences. Probability density function estimation. Random variable simulation. Random processes. Correlation functions. Spectral density. Random processes	3
EE7402	STATISTICAL SIGNAL PROCESSING	Signal Estimation Theory. Properties of Estimators. Sequential estimation methods. Fundamentals of Detection Theory. Detection of Deterministic and Random Signals.	3
EE7403	IMAGE ANALYSIS AND PATTERN RECOGNITION	Image Fundamentals. Image Enhancement and Restoration. Image Analysis. Decision Theory and Statistical Estimation. Classification and Clustering. Dimensionality Reduction.	3

 

 

Graduate courses offered by Master of Science (MSc) Signal Processing (up to Jan 2024 intake)

Specialized Elective Courses (Students are required to take a min of 4 out of all the 5 specialized elective courses)

Course Code	Course Title	Course Content	AUs
EE6101	DIGITAL COMMUNICATION SYSTEMS	Communication signals and baseband transmission. Digital modulation/demodulation. Error correction coding. Spread-spectrum techniques.	3
EE6401	ADVANCED DIGITAL SIGNAL PROCESSING	Discrete-time signal analysis and filter design. Multi-rate digital signal processing. Linear prediction and optimal linear filters. Power spectrum estimation.	3
EE6402	REAL-TIME DSP DESIGN & APPLICATIONS	Digital Filter Implementation Issues. Advanced Arithmetic Techniques for Hardware. Architecture for General Purpose Digital Signal Processor. Peripherals for DSP Applications. Design and Development Tools for DSP Processors. Introduction to VLSI. Algorithms and Architecture for VLSI.	3
EE6403	DISTRIBUTED MULTIMEDIA SYSTEMS	Media and Media Systems. Media Compression and Standards. Media Processing and Storage. Media Transmission and Delivery. Quality of Service on Distributed Multimedia Systems. Multimedia Applications.	3
EE6427	VIDEO SIGNAL PROCESSING	Image and Video Basics. Image and Video Transform Coding. Filtering and Error Resilience for Image and Video. Image and Video Coding Principles and Standards. Recent and Emerging Topics in Image and Video Processing.	3

 

General Elective Courses 

Course Code	Course Title	Course Content	AUs
EE6008	COLLABORATIVE RESEARCH & DEVELOPMENT PROJECT	Project Charter, Design and prototype development, Project implementation, Testing and instrumentation, Project report, Oral presentation, Demonstration	3
EE6010	PROJECT MANAGEMENT & TECHNOPRENEURSHIP	Project Initiation and Planning. Project Scheduling and Implementation. Project Monitoring, Control and Evaluation. Innovation and Entrepreneurship.	3
EE6102	CYBER SECURITY & BLOCKCHAIN TECHNOLOGY	Cyber Security Threat Landscape, Industry 4.0 and Cyber Security, Cyber Security Education, Awareness and Compliance, Cyber Security Planning, Policies and Compliance, Cyber Security Risk Assessments and Biometric-based Security approaches, Public key Infrastructure (PKI), Web Security and role of firewalls and Intrusion Detection, Online Payment, and Cryptocurrencies. Basics of Blockchain technology, Types of blockchain Technology, Blockchain Technology Applications for Industry 4.0, use cases and real-world case studies	3
EE6129	WIRELESS & MOBILE RADIO SYSTEMS	Wireless channel models. Fading and ISI mitigation techniques. Cellular concept and Multiple access techniques. Satellite communications.	3
EE6204	SYSTEMS ANALYSIS	Linear, Dynamic and Integer Programming. Optimization Techniques. Random Processes. Queuing Models. Markov Decision Process.	3
EE6221	ROBOTICS & INTELLIGENT SENSORS	Overview of robotics. Motion planning and control. Mobile robots . Controller hardware/software systems. Sensorsystems and integration.	3
EE6222	MACHINE VISION	Fundamentals of image processing & analysis. Feature Extraction Techniques. Pattern / Object Recognition and Interpretation. Three- Dimensional Computer Vision. Three-Dimensional Recognition Techniques. Biometrics.	3
EE6227	GENETIC ALGORITHMS & MACHINE LEARNING	Review of Combinatorics and Probability. Introduction of Genetic Algorithms. Differential Evolution. Particle Swarm Optimization. Advanced Techniques. Principles of Machine Learning. Paradigms of Machine Learning. Kernel Methods.	3
EE6285	COMPUTATIONAL INTELLIGENCE	Introduction to Fuzzy Logic, Introduction to Fuzzy Sets, Introduction to Fuzzy Inference Systems, Fuzzy Logic Applications, Introduction to Genetic Algorithm, Fundamental Concepts of Artificial Neural Networks and Neural Network Architectures, Neural Network Applications	3
EE6301	SMART BIOSENSORS & SYSTEMS FOR HEALTHCARE	Introduction to biosensors and healthcare; Optical biosensors-fundamentals; Optical biosensors-applications; Biomedical imaging with optical technologies; Introduction to electrical biosensors- fundamentals; Introduction to electrical biosensors- fabrications; Applications of electrical biosensor; Introduction to bio-intelligent systems; Artificial intelligence in medical sensing and imaging	3
EE6341	ADVANCED ANALOG CIRCUITS	Low Noise Circuits, Wide-Bandwidth Amplifiers, Power Amplifiers, Active Filters, DC-DC Converters	3
EE6405	NATURAL LANGUAGE PROCESSING	Traditional: Bag-of-words, Preprocessing, Term weighting scheme, Feature extraction,. Topic modeling , ML classifiers and clustering methods, Evaluation Metrics, Word Embeddings Deep Neural Networks: Graph convolutional network, Seq2Seq, Attention mechanism, Transformers and self-attention, Pretrained Language Models, Fine-tuning (hyper-parameter tuning), Applications (chatbot, machine translation, sentiment analysis, summarisation, classification, generation, auto-complete)	3
EE6428	SPECIAL TOPICS IN SIGNAL PROCESSING	Fundamentals of image analysis, image segmentation and evaluation, object representation and description, feature measurements, shape analysis and texture analysis, and mathematic morphology techniques. Applications and case study.	3
EE6483	ARTIFICIAL INTELLIGENCE & DATA MINING	Structures and Strategies for State Space Representation & Search. Heuristic Search. Data Mining Concepts and Algorithms. Classification and Prediction methods. Unsupervised Learning and Clustering Analysis.	3
EE6497	PATTERN RECOGNITION & DEEP LEARNING	Introduction, probability review, Bayesian Inference, Mixture Models and EM Algorithm, Markov Models and Hidden Markov Models, Sampling, Markov chain Monte Carlo (MCMC), Neural Networks, Deep Learning (CNN, RNN), Training Deep Networks, Deep Network Architectures, Applications, Generative Models, Self-supervised Learning.	3
EE7204	LINEAR SYSTEMS	Input/Output System Models. State Space Representation. Norms of Signals and Systems. Decomposition of Linear Time-Invariant Systems. Linear Feedback Design. Convex Optimization for Linear System Analysis and Design.	3
EE7205	RESEARCH METHODS	Research Preparation and Planning. Research Sources and Review. Quantitative Methods for Data Analysis. Experimental research methods. Academic Writing & Presentation	3
EE7207	NEURAL NETWORKS AND DEEP LEARNING	The key topics to be covered in the context of deep neural networks and deep learning will encompass convolutional neural networks (CNN), modern recurrent neural networks (RNN), the attention mechanism and the transformer, self-supervised learning, graph neural networks, all of which represent the cutting-edge methods in the realm of deep learning. In addition, some typical applications and advanced topics of deep learning will be introduced.	3
EE7401	PROBABILITY & RANDOM PROCESSES	Probability concepts. Random variables. Multiple random variables. Sum of random variables and multidimensional distributions. Random Sequences. Probability density function estimation. Random variable simulation. Random processes. Correlation functions. Spectral density. Random processes	3
EE7402	STATISTICAL SIGNAL PROCESSING	Signal Estimation Theory. Properties of Estimators. Sequential estimation methods. Fundamentals of Detection Theory. Detection of Deterministic and Random Signals.	3
EE7403	IMAGE ANALYSIS & PATTERN RECOGNITION	Image Fundamentals. Image Enhancement and Restoration. Image Analysis. Decision Theory and Statistical Estimation. Classification and Clustering. Dimensionality Reduction.	3

 

Note: the above curriculum is subject to change.

Tuition Fees

Our five MSc programmes (Communications Engineering, Computer Control & Automation, Electronics, Power Engineering and Signal Processing and Machine Learning) are self-financed programmes. 

Students of these programmes are not eligible for Service Obligation/ MOE Subsidies. 

The tuition fees per course (3 AUs) and per dissertation (6 AUs) for admission from August 2025 onwards are shown in the table as follows:

From August 2025 Intake Onwards
Per Course (3 AUs): SGD$5,615.68*	Per Dissertation (6 AUs): SGD$11,231.36*
Minimum Total Tuition Fees (30 AUs): SGD$56,156.80*
SC/SPR Incentive From the academic year starting in 2024, Singaporeans and permanent residents who enrol in self-financed master’s by coursework programmes at NTU will enjoy a $5,000 subsidy, with those in need of financial aid will receive up to $10,000. For academic year starting in 2025, the $5,000 subsidy for Singaporeans and permanent residents will continue to apply. New students enrolled in AY25 in need of financial aid will receive up to $15,000. NTU Alumni Incentive All NTU Alumni will receive an additional 10% tuition fee rebate.

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