AI Architectures, Neural Networks and Green Computing
This module delves into the technical core of artificial intelligence, balancing cutting-edge innovation with environmental sustainability. Students master deep learning architectures, reinforcement learning for smart grid optimisation, and generative modelling for climate simulations. The syllabus harnesses computer vision for satellite biodiversity monitoring, natural language processing for ESG policy analysis, and digital twins for sustainable systems. Remarkably, the curriculum champions “Green AI”, addressing algorithmic efficiency, hardware acceleration, and edge computing for remote intelligent sensors. By mastering carbon-aware strategies and reducing the energy demands of large-scale models, learners are empowered to deploy high-impact, scalable AI solutions without incurring a detrimental carbon footprint.
List of Abbreviations
Introduction to the Module, Introduction to Neural Networks and Deep Learning Architectures
Dr Rawad Hammad
University of East London (London – UK)
02
SESSION
Computer Vision for Satellite Imagery and Biodiversity Monitoring
03
SESSION
Natural Language Processing (NLP) for Global Policy Analysis and ESG Reporting
04
SESSION
Edge AI and IoT: Deploying Intelligent Sensors in Remote Ecosystems
05
SESSION
Green AI: Optimising Algorithmic Efficiency and Computational Complexity
06
SESSION
Sustainable Infrastructure: Reducing the Energy Demand of Large Language Models
07
SESSION
Hardware Acceleration and Carbon-Aware Computing Strategies
08
SESSION
Reinforcement Learning for Smart Grid Management and Energy Optimisation
09
SESSION
Digital Twins: Simulating Sustainable Urban and Industrial Systems
10
SESSION