NICF- Designing Intelligent Edge Computing (SF)
Coursalytics is an independent platform to find, compare, and book executive courses. Coursalytics is not endorsed by, sponsored by, or otherwise affiliated with NUS Institute of Systems Science.Full disclaimer.
This 4-day programme is intended for anyone who wishes to gain specialised knowledge in designing intelligent Edge Computing systems. This course will benefit those working in medical, manufacturing, defence and other industry types. Participants will gain in-depth knowledge as well as practical skills through projects and assessment that reinforce their learning and engage their newly acquired knowledge.
- Design Edge compute systems to provide multi-level intelligence for IoT, transducers and other devices.
- Build data collection, analytics, and decision-making capabilities into these Edge compute systems.
- Integrate machine learning and analytics into Edge Computing to perform decision-making, self-healing, and self-learning This course is part of the Software Systems series and Graduate Certificate in Architecting Smart Systems series offered by NUS-ISS.
Other related courses you might also want to consider are: * NICF - Statistics Bootcamp (SF) * NICF - Python for Data, Ops and Things (SF)
What Will Be Covered
The outline teaching agenda is as follows:
Day 1: Design and Orchestration for Multi-Layer Edge Computing Module 1
- Methodologies and key principles in designing integrated Edge Computing sensor networks
- Capabilities of Edge Computing nodes, sensors, transducers and related technologies
Fog architecture and cloud-based IoT orchestration patterns/practices for Edge Computing systems Workshop 1
Platforms and technologies for configuring and orchestrating Edge Computing topologies
Industrial use case / hands-on exercise to design a layered Edge Computing system Day 2: Data Collection, Analytics, and Decision-making in Edge Computing Module 2
Industry tools and technologies for data collection, transmission, and analytics for IoT
Range of statistical and machine learning modeling techniques, such as linear and logistic regression
Features, pros and cons of the statistical approaches, algorithms and tools Workshop 2
Industrial use case / hands-on exercise applying data collection and analysis techniques to a real-world Edge Computing system
Day 3: Self-monitoring and Self-healing in Edge Computing Module 3
- Quality of Service monitoring, fault management, and self-healing for IoT
Advanced mathematical modelling techniques such as convolutional or recurrent neural networks. Workshop 3
Industrial use case / hands-on exercise to design a self-monitoring and healing Edge Computing system Day 4: Self-learning and Adaptation for Edge Computing Module 4
Self-learning models such as reinforcement learning and online learning algorithms
Selection and optimization of advanced models for self-learning Edge computing applications Workshop 4
Industrial use case / hands-on exercise on designing a self-learning Edge Computing system Final Assessment
Who should attend
This is an intermediate course, suitable for professionals with several years of experience, with an interest or requirement to understand digital marketing and social engagement.
It is applicable for professionals engaged in the following areas.
- Software Architects
- Senior Software Engineers