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 [email protected] for clarification.
 | Programme Director: Associate Professor Lin Zhiping Email: [email protected] Phone: +65 6790 6324 (General Enquiries) |
The following are minimum admission requirements. Meeting these criteria does not guarantee admission, as selection is competitive.
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, for exams taken before January 21, 2026)
≥ 4.5 (internet-based, for exams taken on or after January 21, 2026)
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 Academic Units (AUs), and consists of 39 hours of lectures. Candidates undertaking a 6 AU project are required to submit a dissertation and are advised to select this option only if they have a strong interest in pursuing further research studies.
Option 1 | Option 2 |
| 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 & 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 & 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 ANALYSIS & PROCESSING | This course aims to provide you with the knowledge of video analysis and processing. It will introduce the concepts and theories in video analysis and processing including image and video compression techniques and standards, Artificial Intelligence (AI) models for video analytics, important video analytics tasks and applications, and recent emerging topics in video analysis and processing. Students who are interested in video analytics are encouraged to take this course. The course will equip you with theories and knowledge to advance your future career in the field of video analysis and 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 |
| EE6009 | GRADUATE PROFESSIONAL INTERNSHIP | The purpose of the local internship course is to fill a critical gap in the MSc EEE curriculum by enabling students to translate advanced classroom knowledge such as communications engineering, signal processing, machine learning, power systems, electronics, and automation into real-world applications. It responds directly to industry demand for graduates who are not only technically proficient but also industry-ready, adaptable, and innovative problem-solvers. All full-time MSc students with study option, “Course work only,” and have completed at least 1 semester of study, may take the course. However, students who are under AW (TPGPA or CGPA < 2.50) will not be allowed to take up this course. | 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 & 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 |
| 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 |
| EE6129 | WIRELESS & MOBILE RADIO SYSTEMS | Wireless channel models. Fading and ISI mitigation techniques. Cellular concept and Multiple access techniques. Satellite communications. | 3 |
| EE6156 | COMPUTER ARCHITECTURE | Fundamentals of Computer Design, CPU Design, Instruction Set Architecture, Performance, Buses, I/O and Storage Devices, Memory-System Architecture, Pipelining. | 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 | MULTMEDIA SYSTEMS & PROCESSING | This course aims at providing you with a good understanding of the key concepts, techniques, and applications of multimedia systems and processing. You will learn different aspects of multimedia systems and processing including media types and properties, compression and standards, systems and applications, communications and networking, and emerging topics in multimedia. If you are interested in multimedia systems and processing, you should consider taking this course. This course will equip you with the knowledge for your future advancement in the relevant fields. | 3 |
| EE6406 | ANALYTIC & ENSEMBLE MACHINE 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 |
| 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 | 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 & DEEP LEARNING | Artificial intelligence has made significant progress in recent years, with neural networks and deep learning emerging as the predominant approaches in this expansive field. This course is redesigned to offer a comprehensive introduction to fundamental theories, learning methods, and advanced topics within neural networks and deep learning, specifically tailored for PhD and MEng students. By the end of this course, students will have acquired a profound understanding of these concepts and will be well-equipped to apply their newfound knowledge to address complex challenges within their respective research domains. | 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 |
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 | MULTMEDIA SYSTEMS & PROCESSING | This course aims at providing you with a good understanding of the key concepts, techniques, and applications of multimedia systems and processing. You will learn different aspects of multimedia systems and processing including media types and properties, compression and standards, systems and applications, communications and networking, and emerging topics in multimedia. If you are interested in multimedia systems and processing, you should consider taking this course. This course will equip you with the knowledge for your future advancement in the relevant fields. | 3 |
| EE6427 | VIDEO ANALYSIS & PROCESSING | This course aims to provide you with the knowledge of video analysis and processing. It will introduce the concepts and theories in video analysis and processing including image and video compression techniques and standards, Artificial Intelligence (AI) models for video analytics, important video analytics tasks and applications, and recent emerging topics in video analysis and processing. Students who are interested in video analytics are encouraged to take this course. The course will equip you with theories and knowledge to advance your future career in the field of video analysis and 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 |
| 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 |
| 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 & ENSEMBLE MACHINE 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 |
| EE6407 | 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 |
| 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 & DEEP LEARNING | Artificial intelligence has made significant progress in recent years, with neural networks and deep learning emerging as the predominant approaches in this expansive field. This course is redesigned to offer a comprehensive introduction to fundamental theories, learning methods, and advanced topics within neural networks and deep learning, specifically tailored for PhD and MEng students. By the end of this course, students will have acquired a profound understanding of these concepts and will be well-equipped to apply their newfound knowledge to address complex challenges within their respective research domains. | 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 |
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. |
| EE6428 | SPECIAL TOPICS IN SIGNAL PROCESSING | This course is tailored to special topics from a range of specialized signal processing topics in machine learning. The special topics include, but not limited to one of the following:
-Multimedia Signal Processing, - Adaptive Signal Processing, - Biomedical Signal Processing, - Machine Learning for Signal Processing, - Advanced Topics in Signal Processing |
| EE6430 | SPECIAL TOPICS IN MACHINE LEARNING | This course is tailored to special topics from a range of specialized 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.
FlexiMasters in Signal Processing and Machine Learning
The FlexiMasters in Signal Processing and Machine Learning is designed for engineers and data scientists seeking to advance their expertise in real-time DSP, NLP, ensemble learning, genetic algorithms, and video analysis. This programme builds the competencies needed to address real‑world engineering and data challenges with confidence and applied proficiency.
Credits earned from FlexiMasters in Signal Processing and Machine Learning may be recognised in the MSc Signal Processing and Machine Learning programme, subject to prevailing University’s guidelines and approval. In addition, learners who wish to transfer their course credits into its relevant Master’s programme must ensure their credits meet the current University’s credit transfer policy as follows:
- The grade obtained for the course must be at least a C+, unless otherwise stated.
- The validity period for exemption/ credit transfer of a course is 5 years from the date of award of AUs as reflected in the official result slip/transcript.
- The course must not have been used for exemption/ credit transfer into another programme.
Click here for more information.