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13. Master of Science in Signal Processing and Machine Learning

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Nanyang Technological University (NTU)
Master of Science in Signal Processing and Machine Learning

Singapore, Singapore

DEGREE TYPE

MSc

DURATION

12 months

LANGUAGES

English

PACE

Full time

APPLICATION DEADLINE

EARLIEST START DATE

TUITION FEES

SGD 56,157 *

STUDY FORMAT

On-Campus

* inclusive of 9% GST

Key Summary

About: The MSc in Signal Processing and Machine Learning at Nanyang Technological University (NTU) replaces the MSc in Signal Processing from August 2024. It adds machine learning and AI to the existing signal processing curriculum while keeping all original courses. The programme targets engineers, hardware and software designers, data scientists, R & D managers, and industry planners seeking skills in DSP, ML, AI. Current SP students follow original curriculum.

Career Outcomes: Graduates can work as signal processing engineer, machine learning engineer, data scientist, DSP algorithm developer, software/hardware designer, R&D manager, or industry planner, in telecommunications, robotics, autonomous systems, research labs.

Programme Announcement: MSc (Signal Processing) Renamed to MSc (Signal Processing and Machine Learning)

Starting from the August 2024 intake, the MSc in Signal Processing (SP) programme has been renamed MSc in Signal Processing and Machine Learning (SPML). This change reflects the growing importance of machine learning and AI in modern signal processing, ensuring our programme remains aligned with industry demands and emerging research opportunities.

The updated SPML programme expands on the original SP curriculum by incorporating more AI-related courses while maintaining all the existing courses from the SP programme. This provides students with greater flexibility in course selection and enhances their career prospects in both industry and research.

For Current Students:

The MSc (Signal Processing and Machine Learning) programme is designed for practicing engineers, hardware and software designers, data scientists, R & D managers, and industry planners who seek an understanding of current approaches and evolving directions for DSP and AI technologies. It is also intended for engineers and data scientists who anticipate future involvement in these areas.

Students who were enrolled in the SP programme before this name change will continue to follow the original curriculum, as outlined in the Curriculum Section below. If you have any concerns regarding transcripts or graduation certificates, please refer to the EEE MSc Programme Office at eee_msc@ntu.edu.sg for clarification.

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

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
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	This course introduces the fundamental concepts and methods in pattern recognition and machine learning. Topics covered include Introduction, Bayesian Inference, Mixture Models and EM Algorithm, Markov Models and Hidden Markov Models, Sampling, Markov chain Monte Carlo (MCMC), Neural Networks, Deep Learning, Training Deep Networks, Deep Network Architectures, and Applications.	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
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
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
EE6111	5G COMMUNICATION & BEYOND	This course aims to provide students with a fundamental understanding of the 5G wireless networks. This course is to provide an introduction on the key technologies that support modern mobile communications system 5G and beyond. The course will first provide a quick recap on the basic fundamental of wireless communications, followed by techniques that have been employed in today’s modern communications system (e.g. MIMO, Multi-user MIMO, cooperative communications etc). The course will also cover the solution for machine-type communications, such as IoT. Followed by an overview of future solutions in 6G communications systems. There will also be guest lectures from the industry to bring in latest information for the students.	3

Special Term - General Elective Courses

EE6131	SPECIAL TOPICS IN COMMUNICATION NETWORKS: DESIGN & ANALYSIS OF ALGORITHMS	The course provides a foundation in algorithm design and analysis, focusing on problem-solving and computational efficiency. It prepares students to optimize solutions and implement effective algorithms, with rich discussions, examples and hands-on experiments on design and application of optimization algorithms in communication networks, smart grid, and IoT etc.
EE6230	MULTI-ROBOT SYSTEMS	This course aims to equip you with a comprehensive understanding of the models, principles, methodologies, and technologies for multi-robot systems, focusing on their models, dynamics, control, information exchange, coordination, and optimization. This course is designed for graduate students with backgrounds in robotics, automation, control, computer science, electrical engineering, mechanical engineering, or related fields. By exploring real-world applications such as autonomous vehicles, swarm robotics, and collaborative industrial robots, students will gain both theoretical insights and practical skills essential for careers in robotics R&D, AI-driven automation, and emerging fields like human-robot interaction and autonomous systems. Whether preparing for cutting-edge research or tackling complex challenges in robotics-related industries, this course offers you a vital foundation for future innovation and impact.
EE6231	SPECIAL TOPICS ON REINFORCEMENT LEARNING	Reinforcement learning is inherently interdisciplinary, combining principles from machine learning and control theory. The course aims to equip you with expertise in a cutting-edge AI field, empowering students with the skills needed for innovation, research, and impactful applications in academia and industry. After completing this course, you should understand core concepts and theories in reinforcement learning and implement and evaluate key reinforcement learning algorithms. You can apply the algorithms to real-world problems, especially in autonomous systems, robotics, financial technology, and healthcare.
EE6317	CHIP SECURITY WITH MACHINE LEARNING	The objective of this course is to provide graduate students with an understanding of security challenges in chip design and manufacturing, and how machine learning can be used to address them. Students will learn to identify and mitigate hardware vulnerabilities such as hardware trojans, counterfeit ICs, and side-channel attacks. The course covers the chip development cycle, basic and advanced machine learning models, and their application in chip security analysis. Additionally, students will explore design-for-trust practices to ensure security from design to production. By the end, they will be able to analyze threats and apply secure design principles using machine learning.
EE6430	SPECIAL TOPICS IN MACHINE LEARNING	To propose the new course of “Special topics in machine learning” and to make this course general, so that it can be tailored to one of the special topics in machine learning. The special topics include, but not limited to one of the following: - Reinforcement learning, - Explainable AI, - Large language model, - Natural language processing, - Generative modelling
EE6431	SPECIAL TOPICS IN COMPUTER VISION	Computer vision has become an integral part of our daily lives, yet it presents diverse practical challenges across different application scenarios. This course aims to provide you a comprehensive foundation in computer vision principles and their practical applications, focusing on state-of-the-art solutions driven by deep learning. You will explore milestone models and influential research papers, gaining insights into tackling computer vision tasks under varying constraints of data, labels, and deployment.

Note: the above curriculum is subject to change.

Key Summary

Introduction

For Current Students:

Admissions

Curriculum

Programme Structure

Option 1Coursework Only

Option 2Coursework + Dissertation

Duration

Type of Coursework Programme

Minimum Candidature

Maximum Candidature

General Elective Courses

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)

General Elective Courses

Special Term - General Elective Courses

Program Tuition Fee

Gallery

About the School

Questions

Option 1
Coursework Only

Option 2
Coursework + Dissertation