📚 Syllabus & Course Curriculum
Environmental Science & Sustainability
Module-by-module breakdown of Quantifiable Frameworks for Waste Characterization and Life Cycle Assessment, from foundations to a certified capstone project.
Outline
Leverage state‑of‑the‑art object detection (YOLO) to automate recycling and sorting. • Generate synthetic data and apply transfer learning to overcome scarce labelled datasets. • Integrate RGB, hyperspectral, and infrared imagery for material composition detection.
Outline
Build regression and time‑series models to forecast resource consumption and avoid surplus. • Engineer features from weather, market trends, and IoT sensor data for robust pipelines. • Apply predictive‑maintenance models to reduce equipment scrap and energy waste.
Outline
Implement genetic algorithms and heuristics for smart logistics and route optimisation. • Design reinforcement‑learning agents for dynamic resource allocation. • Map AI outputs to Life Cycle Assessment (LCA) frameworks for quantifiable carbon‑reduction reporting.
Earn government-registered certification in Quantifiable Frameworks for Waste Characterization and Life Cycle Assessment
e-Certificate and e-Marksheet issued on successful completion.
