Executive Education Programmes designed to build capabilities in infocomm and digital business.
Course Planner 2018
Courses with PDUs
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We offer five practice-based graduate programmes focusing on information technology (IT) and data science.
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Clusters of expertise focusing on building leadership, best practice, and capability development in areas of Digital Government and Smart Health.
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MTech EBAC students
Choose 4 courses from the Business Analytics Techniques group, and any 4 other courses. For further information on the Enterprise Business Analytics courses contact Dr. Rita Chakravarti .
MTech KE students
Choose 4 courses from the Knowledge Engineering Techniques group, and any 4 other courses. For further information on the Knowledge Engineering courses contact Dr. Barry Adrian Shepherd.
MTech SE students
Choose any 8 courses. For further information on the Software Engineering courses contact Mrs. Swarnalatha Ashok.
KE5206 Computational Intelligence I
The objectives of the course are to introduce computational intelligence techniques with a focus on Neural Networks and Support Vector Machines (SVM), and explore how these techniques can be used to construct intelligent systems to solve real-world problems such as classification, clustering and prediction. Topics covered in this course include concepts of neural networks, neural networks with supervised and unsupervised learning, neural network modelling, concepts of SVM learning, SVM for linear and non-linear classification and an overview of computational intelligence. There are hands-on exercises and assignments through which students will learn how to construct neural networks and SVM for problem solving.
Pre-requisites: Basic concepts of knowledge-based systems and machine learning
KE5207 Computational Intelligence II
The objectives of this course are to introduce computational intelligence techniques with a focus on Fuzzy Systems, Rough Sets and Evolutionary Computation, and to explore how these techniques can be used to construct intelligent systems to solve real-world problems such as reasoning, decision making and optimization. Topics covered in this course include fuzzy sets, fuzzy logic inference, fuzzy system modelling, fuzzy decision making, rough sets in knowledge discovery, evolutionary computation techniques and genetic algorithms. There are hands-on exercises and assignments through which students will learn how to model fuzzy systems and genetic algorithms for problem solving.
Pre-requisites: Basic concepts of rule-based reasoning and search for optimization
SG5220/SG5221 Independent Work I & II
This two-elective series is aimed at providing students with innovative problem solving skills, and allowing them to conduct experimental project work, evaluating any alternative scenarios, assessing pros and cons of each solution approach and pursuing the best one, and finally rapidly turning news ideas and concepts into real life solution prototypes. Students are expected to work independently with guidance from ISS lecturers. The projects can be sourced or proposed by students or sponsored by ISS lecturers or other organisations. The projects can be of diverse nature: technologies, IT management, scientific application, engineering, and so on.
Pre-requisites: Independent Work I: Approved project proposal; Independent Work II: SG5220 Independent Work I
SG5228 Digital Innovation and Design
Digital Innovation and Design is an emerging discipline, premised on the digital penetration in the 21st century, which has brought about a disruption in the delivery of most services. The distinctive characteristic of this field is that it takes an inter-disciplinary approach to re-design systems for the purpose of value co-creation. With the changing dynamics in the consumer, it is imperative that services leverage latest digital trends such as Big data, Cloud computing, Smart sensors, Artificial Intelligence etc. in order to be able to understand the user and continually adapt to serve them better. This course will introduce participants to frameworks, techniques, and tools that span the innovation cycle from discovery and ideation to implementation and continuous evaluation. Key themes include:
• Research and development of innovation ideas
• Responses to ‘unmet need’ or opportunities to enhance the ‘value proposition’
• User research and market mapping
• Collaboration, engagement, and co-creation
• Prototyping, testing, evaluation, and refinement
• Implementation planning of innovation and design
In addition, the course explores the application of important concepts and practices such as ‘user centred innovation’, ‘design thinking’, ‘ethnographic research’, and ‘customer experience design’. Topics are backed by practice workshops to hone the foundational knowledge and skills for the course.