📚 Syllabus & Course Curriculum
Nanotechnology & Materials Science
Module-by-module breakdown of Air Quality AI: Spatiotemporal Fusion, Concept Drift & Forecasting, from foundations to a certified capstone project.
Outline
Review low‑cost sensor literature and address hardware constraints • Integrate sparse reference stations with dense IoT sensor arrays • Engineer advanced temporal features such as sinusoidal seasonality • Clean noisy readings, handle missing values and calibrate inconsistencies
Outline
Define concept drift in environmental monitoring and its impact • Implement statistical tests and adaptive algorithms for drift detection • Compare global calibration models with dynamic importance weighting • Design remote recalibration workflows for long‑term IoT deployments
Outline
Deploy GRU and Temporal Fusion Transformer models for 24‑hour AQI forecasts • Apply tree‑based ensembles and autoencoders for unsupervised anomaly detection • Structure experiments, baselines, visualizations, and metrics for peer‑review quality • Translate model outputs into actionable alerts and decision‑support insights
Earn government-registered certification in Air Quality AI: Spatiotemporal Fusion, Concept Drift & Forecasting
e-Certificate and e-Marksheet issued on successful completion.
