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The MSc (Computer Control & Automation) programme provides practising engineers with advanced practical tools in the development, integration, and operation of computer-based control and automation systems.
Programme Structure & Duration
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 | Option 2 |
10 courses | 8 courses + dissertation project (30 AUs in total) |
| 4 specialized electives (≥ 12 AUs) | 4 specialized electives (≥ 12 AUs) |
| 6 general electives (≤ 18 AUs) | 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 |
Curriculum
Graduate courses offered by Master of Science (MSc) Computer Control & Automation:
Specialized Elective Courses (Students are required to take a min of 4 out of all the 6 specialized elective courses)
| Course Code | Course Title | Course Content | AUs |
| EE6203 | COMPUTER CONTROL SYSTEMS | Discrete-time system modelling and analysis. Cascade compensation. State-space design methods. Optimal control. Design and implementation of digital controllers. | 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. Sensor systems 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 |
| EE6225 | MULTIVARIABLE CONTROL SYSTEMS ANALYSIS & DESIGN | Basic control algorithms. Model Predictive Control. Multivariable control. Plant parameter estimation. Case studies in process control. | 3 |
| EE6407 | GENETIC ALGORITHMS AND MACHINE LEARNING | 1. Review of Combinatorics & Probability, Introduction to Genetic Algorithms. 2. Differential Evolution. 3. Particle Swarm Optimization. 4. Advanced topics in Evolutionary Algorithms. 5. Introduction to Machine Learning. 6. Supervised Learning Paradigm and Learning Algorithms. 7. Unsupervised Learning Paradigm and Learning Algorithms. 7. Advanced Topics of Machine Learning Theory and Applications | 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 |
| EE6223 | COMPUTER CONTROL NETWORKS | Data Networks in Control and Automation. Local Area Network Concepts and Fieldbus. Application Layer of Fieldbus and MAP. Internetworking and Protocols. Real-time Operating Systems and Distributed Control. Network Performance and Planning. Multimedia in Advanced Control and Instrumentation. | 3 |
| EE6228 | PROCESS MODELING & SCHEDULING | Introduction to Operation Process Modeling – Mathematical Programs, Event-Driven Models (MDP/HMM). Introduction to Scheduling Theory – Flow Shop Problems, Job Shop Problems, AGV Fleet Management in Material Transportation. Introduction to Optimization – Linear and Quadratic Programming, Integer Programming (Branch-and-Bound, Branch-and-Cut), Lagrangian relaxation and decomposition,Metaheuristics, Reinforcement Learning. Introduction to Command Execution – Command Execution System (MES),Closed-loop Resilient Operation Replanning | 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| EE6503 | MODERN ELECTRICAL DRIVES | Introduction. DC Motor Drives. Induction Motor Drives. Synchronous Motor Drives. Servo-Motor Drives. | 3 |
| EE6506 | POWER SEMICONDUCTOR BASED CONVERTER IN RENEWABLE ENERGY SYSTEMS | Module 1: Overview of power electronic circuits and semiconductor devices, Module 2: Power diodes and thyristors as switching devices, Module 3: Power transistors as switching devices 2, Module 4: Protection of devices from overheating di/dt, dv/dt, Module 5: Passive components and magnetics, Module 6: Renewable energy systems | 3 |
| EE6509 | RENEWABLE ENERGY SYSTEMS IN SMART GRIDS | Introduction to Power Systems with Distributed Generation. Distributed Generation. Energy Storage. Smart Grids. | 3 |
| EE6511 | POWER SYSTEM MODELLING & CONTROL | Steady-state Power System Networks. Network Components. Stability Analysis. Power System Control. | 3 |
| EE6534 | MODERN DISTRIBUTION SYSTEM WITH RENEWABLE RESOURCES | Operation of distribution systems. Power quality. Solar power systems. Wind power systems. | 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 in linear systems. Optimum linear systems. Nonlinear systems. | 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.
Additional Information
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$56,156.80* | |
SC/SPR Incentive NTU Alumni Incentive | |
*Inclusive of Goods and Service Tax (GST)
A Goods and Services Tax (GST) of 9% is levied on the import of goods, as well as nearly all supplies of goods and services in Singapore starting 1 Jan 2024.
All fees listed above are in Singapore dollars (S$) 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$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
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