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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.
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 and dissertation, and the other with coursework only. 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.
Option 1 | Option 2 |
| 8 courses + dissertation project (30 AUs in total) | 10 courses (30 AUs in total) |
| 4 specialized electives (12 AUs (min) to 15 AUs (max)) | 4 specialized electives (12 AUs (min) to 15 AUs (max)) |
| 4 general electives (≤ 12 AUs) | 6 general electives (≤ 18 AUs) |
| Dissertation (6 AUs) |
Full-time students are strongly recommended to select the dissertation option, however this option is recommended only for students with a high level of English proficiency. Students taking the dissertation option may take a longer time to complete the programme.
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. All 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:
Specialized Elective Courses
(Students are required to take a minimum of 4 and maximum 5 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 |
| EE6227 | 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 |
| 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 |
| 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 |
| 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 |
| 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 And Fuzzy Systems | Introduction to artificial neural networks. Recurrent and Hopfield Neural Network. Multi-layer perception neural network. Radial basis function neural network. Support vector machines. Self-organizing map neural network. Applications of neural network. Fundamentals of fuzzy logic and fuzzy systems. Takagi-Sugeno (T-S) fuzzy modelling and identification. Stability analysis of fuzzy systems. Applications of fuzzy systems. | 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 |
| 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 |
| EE6341 | Advanced Analog Circuits | Low Noise Circuits, Wide-Bandwidth Amplifiers, Power Amplifiers, Active Filters, DC-DC Converters | 3 |
Tuition Fees
Five MSc programmes (Communications Engineering, Computer Control & Automation, Electronics, Power Engineering and Signal Processing & Machine Learning) are self-financed programmes.
Students of these programmes are not eligible for Service Obligation/ MOE Subsidies.
The tuition fees per module (3 AUs) and per dissertation (6 AUs) for admission from AY2020 onwards are shown in the table as follows:
| Singaporeans (SC) | Singapore PRs (SPR) | International Students (IS) | |||
|---|---|---|---|---|---|
| Per Module | Per Dissertation^ | Per Module | Per Dissertation^ | Per Module | Per Dissertation^ |
| S$3,240* | S$6,480* | S$3,780* | S$7,560* | S$4,320* | S$8,640* |
| Minimum Total Programme Fee | Minimum Total Programme Fee | Minimum Total Programme Fee | |||
| S$32,400 | S$37,800 | S$43,200 | |||
*Inclusive of GST
^The tuition fee for the Dissertation (6 AUs) will be twice of each module fee.
All fees listed above are in Singapore dollars (S$) and subject to annual
revision by the school. The tuition fee is exclusive of living expenses and miscellaneous student fees.
The deposit fee of S$2,000 is payable upon acceptance of the offer and is non-refundable. It will
be deducted from the full tuition fee.
Important Updates:
Below table for intakes starting from August 2024.
| Per Course: S$4,480# | Per Dissertation^: S$8,960# |
|---|---|
| Minimum Total Tuition Fees: S$44,800# | |
| SC/SPR Incentive All Singapore Citizens (SC) and Singapore Permanent Residents (SPR) will be eligible to receive a S$5,000 subsidy, and S$10,000 for needy SC/SPR students. NTU Alumni Incentive All NTU Alumni will receive 10% study incentives in the form of reduction in fees. | |
#Exclusive of 9% GST from 1 Jan 2024 A deposit fee of S$5,000 is payable upon acceptance of the offer and is non-refundable. It will be deducted from the full tuition fee. | |
Awards in MSc Programme
| Awards Title | Awards Description |
|---|---|
| IEEE PHOTONICS & SOCIETY SINGAPORE CHAPTER & THE OPTICAL SOCIETY (OSA) | The Prize (cash award) is awarded to the student with the highest mark in the course EE6122 (Optical Fibre Communications). |
| MICRON GOLD MEDAL | The medal is awarded to the best graduating student with highest aggregate marks in the degree of Master of Science (Electronics) and who has completed the programme within 2 years. |
| PROFESSIONAL ENGINEERS BOARD GOLD MEDAL | The medal is awarded to the graduating student with the highest average marks in the coursework component and who had completed the programme offered by the School of EEE. The awardees are required to have their first degree from the respective prescribed programmes. |
| TEXAS INSTRUMENTS BOOK PRIZE | The book prize (cash award) is awarded to the student with the highest mark in the course EE6402 (Real-time DSP Design and Applications). |